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Project Imaging-X: A Survey of 1000+ Open-Access Medical Imaging Datasets for Foundation Model Development
Zhongying Deng, Cheng Tang, Ziyan Huang, Jiashi Lin, Ying Chen, Junzhi Ning, Chenglong Ma, Jiyao Liu, Wei Li, Yinghao Zhu, Shujian Gao, Yanyan Huang, Sibo Ju, Yanzhou Su, Pengcheng Chen, Wenhao Tang, Tianbin Li, Haoyu Wang, Yuanfeng Ji, Hui Sun, Shaobo Min, Liang Peng, Feilong Tang, Haochen Xue, Rulin Zhou, Chaoyang Zhang, Wenjie Li, Shaohao Rui, Weijie Ma, Xingyue Zhao, Yibin Wang, Kun Yuan, Zhaohui Lu, Shujun Wang, Jinjie Wei, Lihao Liu, Dingkang Yang, Lin Wang, Yulong Li, Haolin Yang, Yiqing Shen, Lequan Yu, Xiaowei Hu, Yun Gu, Yicheng Wu, Benyou Wang, Minghui Zhang, Angelica I. Aviles-Rivero, Qi Gao, Hongming Shan, Xiaoyu Ren, Fang Yan, Hongyu Zhou, Haodong Duan, Maosong Cao, Shanshan Wang, Bin Fu, Xiaomeng Li, Zhi Hou, Chunfeng Song, Lei Bai, Yuan Cheng, Yuandong Pu, Xiang Li, Wenhai Wang, Hao Chen, Jiaxin Zhuang, Songyang Zhang, Huiguang He, Mengzhang Li, Bohan Zhuang, Zhian Bai, Rongshan Yu, Liansheng Wang, Yukun Zhou, Xiaosong Wang, Xin Guo, Guanbin Li, Xiangru Lin, Dakai Jin, Mianxin Liu, Wenlong Zhang, Qi Qin, Conghui He, Yuqiang Li, Ye Luo, Nanqing Dong, Jie Xu, Wenqi Shao, Bo Zhang, Qiujuan Yan, Yihao Liu, Jun Ma, Zhi Lu, Yuewen Cao, Zongwei Zhou, Jianming Liang, Shixiang Tang, Qi Duan, Dongzhan Zhou, Chen Jiang, Yuyin Zhou, Yanwu Xu, Jiancheng Yang, Shaoting Zhang, Xiaohong Liu, Siqi Luo, Yi Xin, Chaoyu Liu, Haochen Wen, Xin Chen, Alejandro Lozano, Min Woo Sun, Yuhui Zhang, Yue Yao, Xiaoxiao Sun, Serena Yeung-Levy, Xia Li, Jing Ke, Chunhui Zhang, Zongyuan Ge, Ming Hu, Jin Ye, Zhifeng Li, Yirong Chen, Yu Qiao, Junjun He
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Summary
Project Imaging-X provides a comprehensive survey of over 1,000 open-access medical imaging datasets, addressing the fragmentation in the field that hinders the development of robust medical foundation models. The authors introduce a Metadata-Driven Fusion Paradigm (MDFP) and an interactive discovery portal to facilitate the integration of disparate datasets into larger, more coherent resources, offering a roadmap for scaling medical imaging corpora.
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Interactive Discovery Portal → implements → Metadata-Driven Fusion Paradigm
confidence 95% · Building on MDFP, we release an interactive discovery portal
Project Imaging-X → proposes → Metadata-Driven Fusion Paradigm
confidence 95% · we propose a metadata-driven fusion paradigm (MDFP)
Metadata-Driven Fusion Paradigm → enables → Medical Foundation Models
confidence 90% · supporting faster data discovery... and more capable medical foundation models
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Abstract
Abstract:Foundation models have demonstrated remarkable success across diverse domains and tasks, primarily due to the thrive of large-scale, diverse, and high-quality datasets. However, in the field of medical imaging, the curation and assembling of such medical datasets are highly challenging due to the reliance on clinical expertise and strict ethical and privacy constraints, resulting in a scarcity of large-scale unified medical datasets and hindering the development of powerful medical foundation models. In this work, we present the largest survey to date of medical image datasets, covering over 1,000 open-access datasets with a systematic catalog of their modalities, tasks, anatomies, annotations, limitations, and potential for integration. Our analysis exposes a landscape that is modest in scale, fragmented across narrowly scoped tasks, and unevenly distributed across organs and modalities, which in turn limits the utility of existing medical image datasets for developing versatile and robust medical foundation models. To turn fragmentation into scale, we propose a metadata-driven fusion paradigm (MDFP) that integrates public datasets with shared modalities or tasks, thereby transforming multiple small data silos into larger, more coherent resources. Building on MDFP, we release an interactive discovery portal that enables end-to-end, automated medical image dataset integration, and compile all surveyed datasets into a unified, structured table that clearly summarizes their key characteristics and provides reference links, offering the community an accessible and comprehensive repository. By charting the current terrain and offering a principled path to dataset consolidation, our survey provides a practical roadmap for scaling medical imaging corpora, supporting faster data discovery, more principled dataset creation, and more capable medical foundation models.
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- Source: https://arxiv.org/abs/2603.27460v1
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Project Imaging-X: A Survey of 1000+ Open-Access Medical Imaging Datasets for Foundation Model Development Zhongying Deng 1,5,∗ Cheng Tang 1,3,∗ Ziyan Huang 1,∗ Jiashi Lin 1,∗ Ying Chen 1,∗ Junzhi Ning 1,∗ Chenglong Ma 2,4,∗ Jiyao Liu 1,4 Wei Li 1,6 Yinghao Zhu 7 Shujian Gao 1 Yanyan Huang 7 Sibo Ju 8 Yanzhou Su 8,14 Pengcheng Chen 1,9 Wenhao Tang 1 Tianbin Li 1 Haoyu Wang 1,6 Yuanfeng Ji 10 Hui Sun 1 Shaobo Min 21 Liang Peng 7 Feilong Tang 1,12 Haochen Xue 1 Rulin Zhou 1 Chaoyang Zhang 2,45 Wenjie Li 2,6,13 Shaohao Rui 2,6 Weijie Ma 2,4 Xingyue Zhao 14 Yibin Wang 2,4 Kun Yuan 1 Zhaohui Lu 6 Shujun Wang 15 Jinjie Wei 1,4 Lihao Liu 1 Dingkang Yang 4 Lin Wang 1 Yulong Li 1 Haolin Yang 1 Yiqing Shen 1 Lequan Yu 7 Xiaowei Hu 16 Yun Gu 6 Yicheng Wu 12 Benyou Wang 17 Minghui Zhang 6 Angelica I. Aviles-Rivero 18 Qi Gao 4 Hongming Shan 4 Xiaoyu Ren 19 Fang Yan 1 Hongyu Zhou 20 Haodong Duan 21 Maosong Cao 1 Shanshan Wang 19,22 Bin Fu 1 Xiaomeng Li 23 Zhi Hou 1 Chunfeng Song 1 Lei Bai 1 Yuan Cheng 24,25 Yuandong Pu 1,6 Xiang Li 26 Wenhai Wang 27 Hao Chen 23 Jiaxin Zhuang 23 Songyang Zhang 1 Huiguang He 28,29 Mengzhang Li 1 Bohan Zhuang 30 Zhian Bai 13 Rongshan Yu 31 Liansheng Wang 31 Yukun Zhou 32 Xiaosong Wang 1 Xin Guo 25 Guanbin Li 33 Xiangru Lin 7 Dakai Jin 34 Mianxin Liu 1 Wenlong Zhang 1 Qi Qin 1 Conghui He 1 Yuqiang Li 1 Ye Luo 35 Nanqing Dong 1 Jie Xu 1 Wenqi Shao 1 Bo Zhang 1 Qiujuan Yan 1 Yihao Liu 1 Jun Ma 36 Zhi Lu 37 Yuewen Cao 1 Zongwei Zhou 38 Jianming Liang 39 Shixiang Tang 1 Qi Duan 40 Dongzhan Zhou 1 Chen Jiang 24,25 Yuyin Zhou 41 Yanwu Xu 16 Jiancheng Yang 42,43 Shaoting Zhang 6 Xiaohong Liu 2,6 Siqi Luo 1,6 Yi Xin 1,2 Chaoyu Liu 5 Haochen Wen 5,32 Xin Chen 44 Alejandro Lozano 10 Min Woo Sun 10 Yuhui Zhang 10 Yue Yao 44 Xiaoxiao Sun 10 Serena Yeung-Levy 10 Xia Li 6 Jing Ke 6 Chunhui Zhang 6 Zongyuan Ge 12 Ming Hu 1,12,† Jin Ye 1,12,† Zhifeng Li 11,† Yirong Chen 1,† Yu Qiao 1,2† Junjun He 1,2,† 1 Shanghai Artificial Intelligence Laboratory; 2 Shanghai Innovation Institute; 3 Shanghai Institute of Optics and Fine Mechanics; 4 Fudan University; 5 University of Cambridge; 6 Shanghai Jiao Tong University; 7 The University of Hong Kong; 8 Fuzhou University; 9 University of Washington; 10 Stanford University; 11 Incept Labs; 12 Monash University; 13 Ruijin Hospital, Shanghai Jiao Tong University School of Medicine; 14 Alibaba DAMO Academy; 15 The Hong Kong Polytechnic University; 16 South China University of Technology; 17 The Chinese University of Hong Kong, Shenzhen; 18 Yau Mathematical Sciences Center, Tsinghua University; 19 Chinese Academy of Sciences; 20 Tsinghua University; 21 Independent Researcher; 22 Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences; 23 The Hong Kong University of Science and Technology; 24 Artificial Intelligence Innovation and Incubation Institute, Fudan University; 25 Shanghai Academy of Artificial Intelligence for Science; 26 Nankai University; 27 The Chinese University of Hong Kong; 28 Institute of Automation, Chinese Academy of Sciences; 29 University of Chinese Academy of Sciences; 30 Zhejiang University; 31 School of Informatics, Xiamen University; 32 University College London; 33 Sun Yat-sen University; 34 Alibaba Group, DAMO Academy, New York, NY, USA; 35 Tongji University; 36 University of Toronto; 37 Department of Psychological and Cognitive Sciences, Tsinghua University; 38 Johns Hopkins University; 39 Arizona State University; 40 Academy for Clinical Innovation and Translation of Shanghai; 41 University of California, Santa Cruz; 42 ELLIS Institute Finland; 43 Aalto University; 44 Shandong University; 45 Xi’an Jiaotong University ∗ ∗ Equal contribution † Corresponding author arXiv:2603.27460v1 [cs.CV] 29 Mar 2026 Abstract Foundation models have demonstrated remarkable success across diverse domains and tasks, primarily due to the thrive of large-scale, diverse, and high-quality datasets. However, in the field of medical imaging, the curation and assem- bling of such medical datasets are highly challenging due to the reliance on clin- ical expertise and strict ethical and privacy constraints, resulting in a scarcity of large-scale unified medical datasets and hindering the development of pow- erful medical foundation models. In this work, we present the largest survey to date of medical image datasets, covering over 1,000 open-access datasets with a systematic catalog of their modalities, tasks, anatomies, annotations, limita- tions, and potential for integration. Our analysis exposes a landscape that is mod- est in scale, fragmented across narrowly scoped tasks, and unevenly distributed across organs and modalities, which in turn limits the utility of existing medical image datasets for developing versatile and robust medical foundation models. To turn fragmentation into scale, we propose a metadata-driven fusion paradigm (MDFP) that systematically integrates public datasets with shared modalities or tasks, thereby transforming multiple small data silos into larger, more coherent resources. Building on MDFP, we release an interactive discovery portal that en- ables end-to-end, automated medical image dataset integration, and compile all surveyed datasets into a unified, structured table that clearly summarizes their key characteristics and provides reference links, offering the community an ac- cessible and comprehensive repository. By charting the current terrain and of- fering a principled path to dataset consolidation, our survey provides a practical roadmap for scaling medical imaging corpora, supporting faster data discovery, more principled dataset creation, and more capable medical foundation models for the biomedical imaging research community. Our project repository can be found at https://github.com/uni-medical/Project-Imaging-X. 2 Contents 1 Introduction5 2 An Overview of Medical Image Datasets7 2.1Total Growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .10 2.2Imaging Dimensionalities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .10 2.3Imaging Modalities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .11 2.4Tasks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .12 2.5Anatomical Regions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .14 2.6Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .15 3 2D Medical Image Datasets15 3.1Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .15 3.2CT Modality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .16 3.3MRI Slices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .17 3.4PET Slices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .19 3.5Ultrasound (US) Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .20 3.6X-Ray Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .21 3.7Optical Coherence Tomography (OCT) Images . . . . . . . . . . . . . . . . . . .22 3.8Fundus Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .23 3.9Dermoscopy Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .24 3.10 Histopathology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .24 3.11 Microscopy Imaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .25 3.12 Infrared Imaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .26 3.13 Endoscopy Imaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .26 3.14 Other Modalities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .27 3.15 Challenge and Opportunity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .28 4 3D Medical Image Datasets29 4.1Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .29 4.2CT Volumes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .30 4.3MRI Volumes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .32 4.4Ultrasound Volumes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .33 4.5PET Volumes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .34 4.6Other 3D Volumes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .36 4.7Challenges and Opportunities . . . . . . . . . . . . . . . . . . . . . . . . . . . . .37 5 Medical Video Datasets39 5.1Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .39 5.2Task . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .39 3 5.2.1Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .39 5.2.2Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .40 5.2.3Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .40 5.2.4Tracking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .41 5.2.5Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .41 5.2.6Registration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .41 5.3Modalities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .42 5.4Anatomical Structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .43 5.5Challenges and Opportunities . . . . . . . . . . . . . . . . . . . . . . . . . . . . .43 6 Paradigm for Dataset Fusion44 6.1Dataset Collection and Processing . . . . . . . . . . . . . . . . . . . . . . . . . .45 6.2MDFP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .46 6.2.1Phase 1: Metadata Harmonization . . . . . . . . . . . . . . . . . . . . . .47 6.2.2Phase 2: Semantic Alignment . . . . . . . . . . . . . . . . . . . . . . . .47 6.2.3Phase 3: Fusion Blueprints. . . . . . . . . . . . . . . . . . . . . . . . .48 6.2.4Phase 4: Dataset Indexing and Community Sharing . . . . . . . . . . . . .49 6.2.5Case Study: Goal-Conditioned Fusion via MDFP. . . . . . . . . . . . .49 6.3Interactive Discovery Portal . . . . . . . . . . . . . . . . . . . . . . . . . . . . .50 7 Discussion51 7.1Limitations in Task Definition and Evolution of Data Engineering Paradigms . . .51 7.2Scarcity of Multimodal Medical Datasets and Constraints in Further Development .51 7.3Challenges and Opportunities in Medical Foundation Models . . . . . . . . . . . .51 8 Conclusion52 A Tables of 2D Medical Image Datasets131 B Tables of 3D Medical Image Datasets131 C Tables of Medical Video Datasets155 4 Figure 1: Evolution of medical foundation models and general domain foundation models. Medical foundation models are mostly trained using millions of images, while advanced general domain ones are trained using billions of natural images. Additionally, most medical foundation models cover only a few modalities like CT and MRI, which may introduce modality-specific bias that constrains clinical applicability. 1 Introduction Medical imaging foundation models hold the promise of significantly advancing clinical decision- making by analyzing diverse medical imaging modalities and executing multiple tasks through a single, pre-trained system. This paradigm parallels the trajectory of advanced models in the domain of natural language processing [1] and computer vision [2, 3, 4, 5], which are trained on extensive and diverse datasets to achieve broad generalization across tasks and applications [6, 7, 8, 9, 10], as depicted in Figure 1. This highlights a similar shift in medical AI from narrow, single-modality, task-specific models toward multi-modal, multi-functional foundation, which could better reflect the complexity of clinical workflows and enhance utility across specialties [11]. Despite this po- tential, current medical imaging foundation models, such as STUNet [12], MedSAM [7], SAM- Med3D [13], SAM-Brain3D [14] and PanDerm [15], are often tailored to well-represented settings, such as a few modalities like computed tomography (CT) and magnetic resonance imaging (MRI), a narrow set of tasks (e.g., segmentation), or limited anatomical regions (e.g., brain, abdomen). Many clinically valuable settings remain less covered, which introduces modality-, task-, and anatomy- specific biases that constrain generalization and clinical applicability. The root challenge lies in data availability and diversity [16, 17]. Most public medical datasets con- tain only thousands of images, e.g., BraTS series [18, 19], which are orders of magnitude smaller than natural image datasets with billions of samples, such as Segment Anything 1 Billion (SA- 1B) [5] and LAION-5B [20]. This substantial difference in the number of training images between the natural image (or general) domain and the medical one is further depicted in Figure 1. Construct- ing large, diverse medical datasets is resource-intensive, requiring specialized imaging equipment, expert annotations, and careful navigation of ethical and privacy constraints. Consequently, the cur- rent dataset landscape is highly fragmented, with data scattered across isolated, narrowly scoped collections [21]. This fragmentation not only limits pre-training scale, but also overlooks opportu- nities to integrate related datasets into richer, more balanced training resources. A promising direction emerging in recent research is dataset integration [22, 23], where multiple smaller datasets with shared modalities, anatomies, or tasks are merged into unified large-scale resources. As shown in Figure 2, the merged datasets can bridge data and models, facilitating the development of foundation models [24]. While this strategy has shown potential, existing efforts 5 Modality:CTA Task:Classification Anatomy:Brain,aorta,... Modality:CT,MRI,PET Task:Segmentation Anatomy:Brain,abdomen,... Modality:CT,MRI Task:Registration Anatomy:Brain,chest,... Modality:MRI Task:Detection Anatomy:Brain,abdomen,... Dataset1 Dataset2 Dataset3 Dataset4 DatasetN Datasilos Withisolatedmodalities, tasks,anatomies,etc. Datasetintegration Ourcontribution,bridgingdataandmodels withunified datataxonomy Foundationmodels Solving multi-modality, multi-task, multi-anatomy challenges Modality:CT,MRI,PET,CTA,... Task:Classification, segmentation,detection,... Anatomy:Brain,chest, abdomen,pelvis,... Targetanatomy:Brain Dataset1 Dataset2Dataset3Dataset4... CTMRICTA... ClassificationSegmentationRegistration... Targetdisease:Livertumor ...... Dataset2 Dataset3Dataset4Dataset5... CTMRICTA... ClassificationSegmentationDetection... Figure 2: Conceptual overview of moving from fragmented medical image data to integrated re- sources for medical foundation models. Our survey addresses the data fragmentation issue in public medical image datasets by introducing a metadata-driven dataset integration paradigm, which is cru- cial for the development of advanced foundation models that can tackle multi-modality, multi-task, and multi-anatomy challenges effectively, ultimately enhancing clinical AI applications. typically focus on specific imaging types or organ systems [25, 26, 27]. Furthermore, when lacking guidance from a comprehensive overview of available datasets, dataset integration risks reinforcing existing biases rather than enabling balanced, general-purpose foundation model development. Given these challenges, a comprehensive survey of medical imaging datasets is urgently needed. Such a survey can illustrate gaps in data coverage, highlight opportunities for dataset integration, and establish a standardized framework for dataset selection and evaluation, which are crucial for the development of robust medical foundation models. A few prior surveys have reviewed medical imaging datasets [28, 29, 30, 31, 32], yet they often lack subject- and image-level statistics, omit many recently released large-scale datasets such as TotalSegmentor [33] and AbdomenAtlas [34], and do not provide a systematic framework that links dataset characteristics to the requirements of foundation model development. To address these limitations, we present the most comprehensive review to date of over 1,000 open- access medical imaging datasets published between 2000 and 2025. We introduce a novel taxonomy to organize datasets by modality, anatomy, task, and label availability. Leveraging this taxonomy, we conduct a gap analysis to identify underrepresented modalities, tasks, and anatomies, establish- ing clear priorities for future dataset creation. Building on these insights, we further propose a metadata-driven fusion paradigm (MDFP) for integrating existing datasets, incorporating it into our interactive discovery portal 2 that enables end-to-end process of fine-grained search, statistical anal- ysis, and dataset integration. We conclude with a forward-looking discussion on the challenges and opportunities toward building truly general-purpose medical imaging foundation models. Our main contributions are summarized as follows: • Comprehensive large-scale survey: We provide the most extensive review to date, cover- ing over 1,000 open-access medical image datasets released over the past 25 years, accom- panied by standardized and detailed metadata. • Integration paradigm: We establish a structured taxonomy and present a metadata-driven fusion paradigm (MDFP), effectively scaling-up existing medical imaging data for medical foundation model development by integrating datasets with shared characteristics. • Interactive discovery portal: Based on the unified taxonomy and the MDFP, we build an interactive discovery portal that enables automated and fine-grained dataset search, integra- tion, and statistical analyses by modality, anatomy, task, and label type. • Gap analysis: We identify underrepresented modalities, anatomical regions, and tasks, highlighting critical limitations that hinder the development of future foundation models. • Accessible community resource: We release the portal, all surveyed dataset information, related Python toolkit, and a merged large-scale dataset for public use, offering a transpar- ent and practical resource for the research community. 2 https://tchenglv520.github.io/medical-dataset-browser/ 6 The remainder of this paper is organized as demonstrated in Figure 3. Section 2 offers a high-level panorama of the landscape of over 1,000 open-access medical image datasets, analyzing their dis- tribution across modalities, tasks, and anatomical regions. Section 3 zooms in on two-dimensional (2D) image datasets, providing a modality-specific breakdown and revealing extreme fragmentation and a long-tail distribution. Section 4 covers three-dimensional (3D) volumetric datasets, focusing on their unique clinical value and challenges of high cost and annotation complexity. Section 5 reviews video datasets, highlighting their role in spatiotemporal analysis. To address the pervasive data fragmentation, Section 6 introduces our Metadata-Driven Fusion Paradigm (MDFP), a system- atic workflow for integrating disparate datasets, and the corresponding interactive discovery portal for automated and effective dataset integration. Section 7 discusses broader challenges and future directions. Finally, Section 8 concludes the survey. Section 2 Overview of Datasets Scope 1000+ datasets Comprehensive data coverage Generation Section 3 2D Datasets Section 4 3D Datasets Section 5 Video Datasets Tasks : •Generation •Classification •Detection •Segmentation Organs : •Retina •Breast •Brain •Colon Datasets : •EyePACS •MedMNIST •OCTA-500 •CheXmask Licenses Dimensions 2D 3D Video DetectionSegmentation Registration Classification Tracking Reconstruction Tasks : •Segmentation •Detection •Registration •Reconstruction Organs : •Brain •Liver •Prostat •Heart Datasets : •AutoPET •Narratives •M-WHS •CT-RATE Tasks : •Detection •Segmentation •Classification •Tracking Organs : •Colon •Esophagus •Stomach •Heart Datasets : •EndoVis •AVOS •Cataract-1K •SurgVisDom Modalities Section 6 Dataset Fusion Paradigm Section 7 Challenges & Discussion Metadata Harmonization Fusion Blueprint Semantic Alignment Dataset Indexing & Community Sharing Data Gaps & Bias Opportunities Evaluation for FMs Tasks Organs Modalities : •Pathlogy •X-Ray •CT •MRI Modalities : •MRI •CT •PET •Ultrasound Modalities : •Endoscopy •Ultrasound •Microscopy •RGB Figure 3: Overview of the survey. We first introduce the overview of the medical imaging datasets, followed by three sections detailing 2D, 3D, and video datasets. We further implement integration strategies to merge the datasets for large-scale resources, which can potentially be leveraged for the development of foundation models. Finally, we discuss the challenges for foundation model development. 2 An Overview of Medical Image Datasets This section provides an overview of 1000+ medical image datasets released between 2000 and 2025, covering diverse anatomical structures, modalities, and tasks as illustrated in Figure 4. These datasets are compiled from major public repositories (The Cancer Imaging Archive 3 , etc.) and recent challenge sites (Grand Challenge 4 , etc.) followed by deduplication, manual verification of landing pages/licences, and metadata normalization, ensuring comprehensive coverage. We leave details of dataset collection process in Section 6. To better organize the landscape, we adopt the taxonomy in Figure 5. Specifically, we begin by grouping medical imaging datasets by imaging dimensionality (2D, 3D, and video). Within each di- mensionality, we further categorize datasets by imaging modality. Finally, within each modality, we subcategorize datasets by task (e.g., segmentation, classification) and anatomical region. This pro- vides a comprehensive basis for the analyses below, and aligns well with foundation model training needs, where dimensionality influences backbone architectural design, modality reflects acquisition physics and clinical use, task determines supervision signals and anatomical diversity shapes gener- alization in clinical practice. 3 https://w.cancerimagingarchive.net 4 https://grand-challenge.org 7 Dermoscopy MRI CTCT Pathology Endoscopy X-Ray Skin X-Ray MRI Bone and Spine Chest Abdomen Other Eyes CT CT CT CT MRI MRI CT MRI Head and Neck CT Head and Neck CT Fundus photo 0 25 50 75 100 125 150 175 Number of Datasets Yearly Data Number and Dataset Count 20002010201520202025 Year 10 10 10 10 10 10 10 10 Total Data Number Data Number — Bar Dataset Count Data Number — Line 7 6 5 4 3 2 1 0 0 25 50 75 100 125 150 175 Number of Datasets Yearly Data Number and Dataset Count 20002010201520202025 Year 10 10 10 10 10 10 10 10 Total Data Number Data Number — Bar Dataset Count Data Number — Line 7 6 5 4 3 2 1 0 Ultrasound segmentationsegmentation segmentation segmentation segmentationsegmentation classificationclassificationclassification detection detectiondetection segmentation segmentation detectiondetection detection nevusmelanomabenign Dermoscopy MRI CTCT Pathology Endoscopy X-Ray Skin X-Ray MRI Bone and Spine Chest Abdomen Other Eyes CT CT CT CT MRI MRI CT MRI Head and Neck CT Head and Neck CT Fundus photo 0 25 50 75 100 125 150 175 Number of Datasets Yearly Data Number and Dataset Count 20002010201520202025 Year 10 10 10 10 10 10 10 10 Total Data Number Data Number — Bar Dataset Count Data Number — Line 7 6 5 4 3 2 1 0 0 25 50 75 100 125 150 175 Number of Datasets Yearly Data Number and Dataset Count 20002010201520202025 Year 10 10 10 10 10 10 10 10 Total Data Number Data Number — Bar Dataset Count Data Number — Line 7 6 5 4 3 2 1 0 Ultrasound segmentationsegmentation segmentation segmentation segmentationsegmentation classificationclassificationclassification detection detectiondetection segmentation segmentation detectiondetection detection nevusmelanomabenign Figure 4: Overview of medical imaging datasets: representative modalities by anatomical region (left), dataset distribution across modalities, anatomical regions, and tasks (upper right), and tempo- ral trends in dataset numbers (lower right). The resulting manifest underpins all figures in this section. We are particularly interested in the number of images in these datasets (see the right panel of Figure 4), as it strongly influences the effectiveness of foundation model pre-training. Following this principle, we first present the total growth over time and then analyze distributions by imaging dimensionality, modality, task, and organ. 2D 3D Video Pathlogy CT X-ray MRI Fundus · CT MRI Pathology Ultrasound · Endoscopy Microscopy Ultrasound · Classification Segmentation Detection Generation Brain Lung Liver Breast Retina Classification Segmentation Detection Generation Tracking Brain Lung Liver Breast Retina Classification Segmentation Detection Generation Registration Brain Lung Liver Breast Retina Figure 5: Taxonomy of medical imaging datasets across data dimensions, modalities, tasks, and anatomical organs. 8 (a)(b) (c)(d) (e) Figure 6: The overview of image number in medical image datasets released from 2000 to 2025. (a) Total image number; Image number of different (b) dimensions, (c) modalities, (d) tasks, and (e) top five organs. 9 (a) (b) (c) (d) Figure 7: The distribution of (a) imaging dimensionalities, (b) modalities, (c) tasks, and (d) anatom- ical regions. 2.1 Total Growth We first examine the annual count of released imaging data to gain insight into the temporal evolution of open-access medical image datasets. Figure 6(a) illustrates the number of imaging items publicly released per year from 2000 to 2025, with clear inflection after 2012 and another surge after 2023. The first phase tracks the rise of deep learning methods [35], which increased demand for extensive, curated training data. The recent surge beginning in 2023 reflect the adoption of self-supervised and large-scale foundation models, which benefits from scale even with limited labels. These advances highlight the centrality of massive datasets for enabling foundation-level models, motivating the medical imaging community to collect substantially larger resources in pursuit of general-purpose medical AI. For example, AbdomenAtlas [34] aggregates 1.5 million 2D CT images and 5,195 3D CT volumes. CT-RATE [36] introduces 25,692 non-contrast 3D chest CT scans from 21,304 unique patients. These datasets rank among the largest public medical imaging resources. Nonetheless, the scale of existing medical imaging datasets, particularly in terms of 3D volumes, remains orders of magnitude smaller than data resources in natural image and language domains, where training corpora typically contain trillions of tokens [37]. Given the prohibitive cost of curating trillion- scale medical datasets, an alternative and more practical strategy is to integrate multiple existing datasets into larger, heterogeneous corpora. This observation motivates our dataset fusion paradigms (Section 6). 2.2 Imaging Dimensionalities Two-dimensional images have height and width as their two dimensions, while three-dimensional volumes add a depth axis; videos are time-ordered 2D frames with temporal continuity. Figure 6(b) 10 presents the total number of 2D images, 3D volumes, and videos released between 2000 and 2025, and Figure 7(a) shows the distribution of them. Two-dimensional images dominate in absolute scale, especially after 2023 (Figure 6(b)), reflecting the wide use of 2D images for medical applications. This dominance has practical and methodologi- cal roots: 2D images are easier to store and share; patch extraction from histopathology whole-slide images (WSIs) multiplies sample counts [38, 39]; and many long-standing benchmarks target 2D tasks [40, 41]. In contrast, 3D and video data remain comparatively scarce and show slower growth, largely due to higher acquisition costs and storage constraints, and the complexity of curation and annotation [42]. Despite lower availability, 3D and video data are often more clinically informative for diagnosis and treatment planning, particularly in radiology, as they capture volumetric context and temporal dynamics that 2D cannot [43]. Increasing the availability of high-quality 3D and video datasets is therefore a priority for advancing clinically useful foundation models. 2.3 Imaging Modalities MRI c MRI c PathologyCT X-Ray Ultrasound Fundus photo Dermoscopy Endoscopy MRI c MRI c PathologyCT X-Ray Ultrasound Fundus photo Dermoscopy Endoscopy Figure 8: Representative modalities in medical imaging datasets. Clinical practice employs different technologies and techniques to acquire medical images; these techniques are known as imaging modalities, as depicted in Figure 8. Each modality is designed to capture specific anatomical, functional, or molecular characteristics of the human anatomical regions/structures, and plays a critical role in clinical diagnosis and disease monitoring. Some of the most commonly used imaging modalities include: • X-ray imaging is among the oldest and most widely used techniques [44], capturing 2D projections of internal structures using X-rays. It is widely applied to detect hard tissues such as bone fractures, lung infections, or dental issues. • Computed Tomography (CT) visualizes internal structure of human body with tomo- graphic acquisition of many slices to form 3D volumes. CT offers high spatial resolution and speed, making it particularly valuable in trauma, oncology, and cardiovascular imag- ing [45]. 11 • Magnetic Resonance Imaging (MRI) generates high-contrast images of soft tissues using strong magnetic fields and radiofrequency pulses. It is frequently used in neuroimaging and visualizing internal organs such as the heart, liver, and kidneys [46], with sub-modalities including T1, T2, FLAIR, DWI, and fMRI. • Ultrasound imaging leverages high-frequency sound waves to visualize soft tissues and fluid-filled structures. It is safe, portable, and economical, and is widely used in obstetrics, cardiology, and abdominal imaging. • Positron Emission Tomography (PET) uses a radioactive tracer to detect diseased cells, featuring in functional imaging. It is commonly used for diagnosing dementia, cancers, and assessing heart conditions [47]. • Pathology imaging applies advanced microscopy and digital slide scanning to achieve ultra-high-resolution reconstruction and computational analysis of tissue. Beyond serving as the gold standard for histological classification, grading, and definitive cancer diagno- sis, it is increasingly central to biomarker discovery, prognostic modeling, and AI-driven computational pathology [48]. • Endoscopy employs a mini-camera embedded in a flexible tube, which will be inserted into the gastrointestinal tract, respiratory pathways, or other body orifices to directly vi- sualize internal organs and cavities [49]. It is widely applied in diagnostic inspection and interventional procedures. • Fundus photography captures detailed images of the retina at the back of the eye. It is essential in ophthalmology for diagnosing and monitoring diabetic retinopathy, age-related macular degeneration, glaucoma, and other retinal diseases [50]. • Dermoscopy provides non-invasive, magnified views of skin lesions, enabling observation of both skin surface and superficial layers. This technique reveals fine structural details of pigmented lesions and thereby improves the diagnostic accuracy of skin lesions, particu- larly for melanoma [51]. • Other modalities include mammography for breast screening, fundus fluorescein angiog- raphy (FFA) and optical coherence tomography (OCT) for visualizing internal structures of the eyes, as well as non-imaging modalities that record electrical activity such as the electrocardiogram (ECG) for the heart, the electroencephalogram (EEG) for the brain, and electromyography (EMG) for muscle response. Figure 6(c) shows the image number of the top six modalities from 2000 to 2025. Prior to 2023, CT, pathology, and MRI account for the majority of images. Post-2023 growth is especially pro- nounced in pathology imaging, X-ray, fundus photography, and microscopy. As shown in Fig- ure 7(b), pathology datasets contain substantially more images than other imaging modalities be- cause the gigapixel-scale WSIs are often divided into thousands of patches, each used as a separate image for analysis [52]. The inherently multi-scale nature of pathology, spanning cellular morphol- ogy to tissue-level architecture, further increases patch generation by requiring sampling at multi- ple magnifications. Moreover, the diversity of staining protocols and specimen types adds further heterogeneity and volume [53]. These factors provide pathology with an unmatched reservoir of fine-grained image data that underpins the training of foundation models [48]. X-ray and CT also benefit from clinical ubiquity and high throughput [45]. MRI accounts for about 10.4% of the total number of images due to its effectiveness in visualizing soft tissues. Despite being radiation-free, MRI imaging data grows relatively slowly due to cost, longer acquisition, and complex multi-sequence labeling. In addition, fundus photography, microscopy, dermoscopy, and ultrasound are widely used and produce a significant number of images. However, other modalities like PET, mammography, and endoscopy remain comparatively less available in open data, which may limit the ability of foundation models trained on public corpora to fully address modality- specific clinical tasks. 2.4 Tasks Medical image datasets can be collected and curated to address a wide range of tasks, each target- ing specific aspects of image analysis and interpretation in computer-aided diagnosis and clinical workflows. These tasks include, but are not limited to, segmentation, classification, registration, generation, detection, and tracking. 12 Segmentation tasks involve assigning a class label to each individual pixel in 2D images or voxel in 3D volumes. The goal is to delineate anatomical structures or pathological regions of interest, such as organs, tumors, and lesions, allowing for precise spatial localization and quantitative analysis. For example, in abdominal MRI, segmentation can distinguish between the liver and kidneys, facilitating downstream analysis like volume estimation or disease monitoring. Classification tasks aim to categorize an entire medical image or a specific region within it into predefined classes. This could involve distinguishing between healthy and diseased states, grading the severity of a condition, or identifying the presence of particular disease types. For instance, in brain MRI, classification might involve determining whether an image corresponds to a cognitively normal subject, someone with mild cognitive impairment, or a patient with Alzheimer’s disease. Registration refers to the process of aligning two or more images into a common coordinate sys- tem. This is particularly important when comparing scans from different time points (longitudinal analysis), modalities (e.g., MRI and PET), or subjects (for population studies). Registration tech- niques compute spatial transformations to ensure that anatomical structures in one image accurately correspond to those in another. Accurate registration is essential for tasks like image fusion, growth tracking, or mapping patient data to standardized anatomical atlases. Generation tasks typically use models to synthesize new medical images, often conditioned on specific attributes or constraints. This can help augment training datasets, simulate rare disease appearances, or recover missing modalities. Detection focuses on efficient identification and localization of specific pathological findings with bounding boxes, such as lung nodules in CT, surgical instruments in endoscopic videos, or cancer cells in pathology slides. Tracking monitors the movement or evolution of anatomical structures or lesions across image se- quences or time-series data, which is critical for assessing disease progression or treatment response. Reconstruction transforms incomplete or indirect raw data obtained from sensors into a meaningful image, which often involves solving inverse problems and addressing low-level vision tasks. Regression predicts continuous, quantitatively meaningful targets from medical images or se- quences, such as physiologic indices (ejection fraction), image-derived biomarkers (arterio- lar–venular ratio), severity/quality scores, or voxel-wise physical fields (dose), supporting precise monitoring, prognosis, and treatment planning. Localization seeks to identify specific anatomical points or landmarks in an image, such as corners of bones or key organ boundaries, to support diagnosis, measurement, registration, or treatment planning. Beyond these, vision-language tasks are emerging thanks to the rapid development of multimodal large language models. For instance, visual question answering (VQA) aims to answer questions about given images in natural language, while captioning and report generation produce free-text or structured descriptions from images or image series, in different levels of detail. In Figure 6(d), from the task-wise statistics, classification and segmentation account for the largest share of released images over the past decade, while datasets tagged for generation exhibit a marked uptick after 2023, showing the strong community interest in applying general-purpose generative AI for advanced medical image analysis. In contrast, other tasks like registration, detection, and tracking, remain a relatively small number of images over time. We stress, however, that the ap- parent imbalance is more indicative of practical constraints than of community priorities, because counts are shaped by mixed factors of label economics (e.g., per-image classification labels are com- paratively inexpensive) as well as acquisition and annotation burden (e.g., tracking requires videos with temporal labels [54]; registration often lacks easily verifiable ground truth and may depend on multi-timepoint/multimodal data [55]). Figure 7(c) further presents the imbalanced distribution of these tasks, where generation, classifi- cation, and segmentation tasks are more extensively studied compared to the remaining tasks. This imbalance may not be ideal for training a general-purpose AI excelling in these less-represented tasks. 13 （a）（b）（c） Figure 9: Representative samples of three medical image analysis tasks: (a) classification, (b) seg- mentation, and (c) detection. Each column shows images from diverse modalities and clinical ap- plications, illustrating the characteristic outputs of the respective task types. Visual examples in Figure 9 intuitively demonstrate the distinct outputs and clinical relevance of each task, highlighting how the same imaging modalities can serve different analytical purposes depending on the problem at hand. 2.5 Anatomical Regions Figure 10: Anatomical structures of medical image datasets. We also show the total number of datasets and images for each anatomical structure. Anatomical regions, illustrated in Figure 10, are specific, named areas of the human anatomical re- gions used to organize and describe structures. This subsection analyzes the datasets from a compact set of anatomical regions, identified in two steps. First, we align each dataset’s native labels with standard medical vocabularies. Second, we group those mapped labels into a concise set of anatom- ical regions/structures that are consistently reported across public datasets and align with major clinical workload and benchmark. This choice favors comparability and coverage across sources. While finer-grained systems are possible, they are unevenly annotated in the open-access datasets. Figure 6(e) tracks medical imaging data counts for common target organs. We observe that brain and lung contribute the largest volumes of images before 2023. Starting in 2023, there is an abrupt and 14 pronounced surge in several modalities, most notably brain, liver, lung, and breast, while retina does not exhibit a comparable surge. This pattern highlights shifting research priorities toward clinically significant organs. Figure 7(d) shows the distribution of anatomical regions. Notably, the number of fully body, retina, breast, brain, lung, and colon images significantly exceeds that of other regions, highlighting a strong research emphasis on with high clinical and societal impact, including Alzheimer’s disease, diabetic retinopathy, and common cancers such as breast, lung, and colorectal cancer. In contrast, other anatomical regions are less represented, such as the foot, blood, heart, bowel, shoulder, huerus, forearm, etc. Such undercoverage often stems from practical challenges such as limited accessibility, lower disease prevalence, or the complexity of imaging certain anatomical sites. 2.6 Summary The public landscape of open medical imaging is distinctly long-tailed: many small, tightly scoped datasets coexist with a smaller set of large hubs, with pronounced skew toward 2D images, a subset of modalities (notably pathology, X-ray, CT, MRI), and a handful of organs (brain, lung, liver, breast). Task labels are likewise imbalanced where classification and segmentation dominate while other tasks are comparatively underrepresented, largely reflecting practical constraints such as label economics, acquisition burden, and the scarcity of ground truth for certain tasks. These patterns imply that scale for general-purpose models is attainable, but not via nai ̈ ve concatenation: it requires careful normalization of counting conventions, balanced sampling across modalities and organs, and task-aware objectives to avoid amplifying existing biases. Given this heterogeneity and uneven distribution, it is increasingly crucial to effectively utilize all these datasets for training medical foundation models. Specifically, recent medical foundation mod- els across subdomains have been trained by integrating multiple public datasets within a modality or organ. Examples include backbone model for ophthalmology [56, 57], histopathology [58], radio- graphy [59, 60], segmentation-focused families in 2D [7] and 3D [13, 14], and even autoencoders for generative models [61, 62]. In short, the very imbalances mapped above become design signals for assembling data and objectives. With the proposed taxonomy and metadata-driven fusion, this paper provides a principled path from fragmented public datasets to scalable, diverse, and clinically relevant training distributions for medical foundation models. 3 2D Medical Image Datasets We have collected 502 2D medical image datasets. In aggregate data count, 2D image far exceeds 3D volumes and video frames. We partition them into 475 labeled and 45 unlabeled datasets. Labeled datasets are analyzed by modality, task, and anatomical focus; for unlabeled datasets which lack explicit task definitions, we summarize modality and anatomy. 3.1 Overview Figure 11 shows the distributions of different modalities, anatomical regions/structures, and tasks for 2D labeled images, which represent clear long-tail distributions. In terms of modality, pathology and X-ray dominate, followed by CT, MRI, and fundus photogra- phy. Together these account for the majority of images. Other modalities such as endoscopy are less representative. In terms of anatomy, large shares concentrate on full-body/multi-structure views and a few organs with mature screening pipelines, e.g., retina, breast, and brain, followed by lung and colon. By contrast, datasets targeting uterus, heart, esophagus, limb joints, and small substructures (e.g., nodules) remain underrepresented, indicating opportunities for targeted curation. The dom- inating tasks include generation, classification, segmentation, regression, and detection. However, other tasks, e.g., registration, tracking, localisation, reconstruction, and visual question answering, have much fewer images. Figure 12 demonstrates representative examples of the collected 2D med- ical image datasets across different modalities and anatomical regions. 15 (a) (b) (c) Figure 11: The distribution of different (a) modalities, (b) anatomical structures, and (c) tasks for for 2D labeled datasets. Each slice of the pie chart shows the percentage and the actual number of images. 3.2 CT Modality CT is a cornerstone of radiological imaging, providing detailed cross-sectional views of the body. In 2D datasets, CT images are typically axial, sagittal, or coronal slices extracted from 3D vol- umes. A predominant characteristic of CT datasets is their extensive use in segmentation tasks. These tasks can be broadly categorized into delineating anatomical structures, such as organs for surgical planning, and identifying pathologies, such as tumors or hemorrhages for diagnosis and treatment monitoring. This makes CT datasets highly valuable for a wide range of clinical applica- tions. Among 2D labeled CT datasets, 39 provide CT slices (see Table 7), totaling approximately 1.4 million images. Scale varies dramatically: from small, specialized collections such as The Visible Human Project (#14) with only 2 images, to large-scale resources like RSNA Intracranial Hemorrhage Detection (#6) with 874,000 images. CT Datasets by Anatomical Regions/Structures. A clear trend in the distribution of CT datasets is the focus on specific anatomical regions. Datasets related to the brain are the most represented in terms of image volume, primarily due to a single large-scale dataset (#6). Lung-related datasets are the most numerous, driven by research in COVID-19 and cancer screening. Conversely, data for abdominal and other structures remains relatively scarce, highlighting potential gaps in data availability for developing models for those areas. 1) Lung (11 datasets,∼60,400 images). A significant portion of the datasets is dedicated to the lungs, focusing on tasks like cancer classification in the National Lung Screening Trial (#5) and COVID-19 classification in datasets such as COVID-19-CT SCAN IMAGES (#8) and SARS-COV-2 Ct-Scan Dataset (#12). Segmentation is also a key task, as seen in CT Medical Images (#4). These datasets are characterized by distinct visual patterns, such as ground-glass opacities for COVID-19 and well- defined nodules in cancer screening, making them ideal for developing specialized classifiers. 2) Brain (5 datasets, ∼874,400 images). Brain datasets constitute the largest collection by image count, dominated by the RSNA Intracranial Hemorrhage Detection dataset (#6) for localization tasks. Other datasets like Brain CT Images with ICH Masks (#17) focus on segmentation, while smaller sets like Cranium Image Dataset (#11) are used for detection. 3) Abdomen/Pelvis (7 datasets,∼1,500 images). This category covers organs such as the kidney, pan- creas, colon, and prostate. Key tasks include segmentation and classification of tumors in datasets like CMB-CRC (#18) for colorectal cancer and segmentation of kidneys and pancreas in the QUBIQ challenges (#35). These datasets are typically small, limiting their use for training large-scale deep learning models. They often feature multiple organs with subtle boundaries and variable shapes, making multi-organ segmentation a significant challenge despite limited data availability. 4) Full-Body/Multistructure (5 datasets,∼454,400 images). These datasets provide data from multi- ple anatomical regions or cell structures, making them suitable for pre-training generalizable models. Notable examples include RadImageNet (#16), a large-scale classification dataset with 34 anatomic categories, and MedMNIST (#13), which contains diverse 2D slices for educational and research purposes. Their diversity across different anatomical regions/structures helps models learn a more generalized representation of CT imaging characteristics, reducing the risk of overfitting to a specific anatomy. 16 5) Others (11 datasets,∼11,000 images). This group comprises datasets for various other anatomical regions/structures or those without a specified structure. It includes specialized collections such as 5K+ CT Images on Fractured Limbs (#2) for limb fracture segmentation and Head CT Image Data (#25) for classification. Datasets with non-specific structures, like RIDER Phantom PET- CT (#30) for calibration, are also in this category. CT Datasets by Tasks. The distribution of datasets is heavily skewed towards classification, which accounts for a large volume of images. Detection and localization tasks are dominated by a single large dataset, while segmentation and reconstruction datasets are generally smaller in scale. 1) Classification (12 datasets,∼513,900 images). Classification is the most common task, especially for pulmonary applications spurred by the COVID-19 pandemic, with datasets like COVID-CT (#9) and SARS-COV-2 Ct-Scan Dataset (#12). Large multi-purpose datasets like RadImageNet (#16) and MedMNIST (#13) also contribute significantly to this category. Oncology is another major focus, with datasets such as the National Lung Screening Trial (#5) for lung cancer. These tasks often involve distinguishing between different diseases or staging disease severity from a single representative slice. 2) Segmentation (9 datasets,∼2,100 images). Segmentation datasets are diverse but generally small. They cover organ segmentation, such as in the QUBIQ challenges (#35), lesion segmentation in Brain CT Images with ICH Masks (#17), and quantitative imaging in Finding and Measuring Lungs in CT Data (#24). Segmentation in CT is crucial for quantitative analysis, such as measuring tumor volume or assessing organ health, moving beyond simple qualitative assessment. 3) Detection/Localization (2 datasets, ∼874,100 images). This task category is dominated by the RSNA Intracranial Hemorrhage Detection dataset (#6), which contains 874,000 slices with hem- orrhage annotations. The only other dataset in this category is the much smaller Cranium Image Dataset (#11), also for hemorrhage detection. This task is often a precursor to segmentation and is critical in large-scale screening programs where anomalies need to be quickly identified. 4) Reconstruction (1 dataset, 28 images). The LoDoPaB-CT dataset (#1) is the sole entry dedicated to reconstruction, specifically for sparse-view reconstruction challenges. 5) Multi-task datasets (3 datasets, ∼500 images). A few small datasets are designed for multiple tasks. For example, CMB-CRC (#18) provides data for both segmentation and classification of colorectal cancer, while CMB-PCA (#22) is for classification and prediction in prostate cancer. 6) Others (12 datasets, ∼11,200 images). The remaining datasets are for other specific tasks or have no specified task. This includes AREN0534 (#3) for estimation and LDCTIQAC2023 (#26) for registration. A significant number of datasets, such as those from the TCIA archive like CPTAC- LSCC_CT_PET (#23) and Prostate-MRI (#32), have no explicit task listed and may be used for a variety of research purposes. 3.3 MRI Slices MRI offers superior soft-tissue contrast compared to CT and does not introduce ionizing radia- tion, making it ideal for neurological, musculoskeletal, and oncological imaging. A key feature of MRI datasets is their multi-contrast nature; a single study often includes multiple sequences (e.g., T1-weighted, T2-weighted, FLAIR) that highlight different tissue properties. This multi-channel information provides a rich basis for tasks like tumor segmentation and tissue characterization, though it also presents a challenge in fusing the information effectively. Our analysis of 24 di- verse MRI and multimodal imaging datasets (Table 8) reveals important trends in dataset develop- ment across modalities and clinical applications. In total, there are approximately 722,400 images, with significant variations in scale: from small, specialized collections, such as The Visible Human Project (#42) with only 2 images, to large-scale resources like RadImageNet (Subset: MR) (#56) with 673,000 images. MRI Datasets by Anatomical Regions/Structures. A clear trend in the distribution of MRI datasets is the focus on specific anatomical regions, alongside a growing number of large-scale, multi-structure collections suitable for pre-training generalizable models. Datasets related to the brain are a common focus, though typically smaller in scale. Abdominal and pelvic datasets are also 17 classification detection segmentation Landmark localization X-ray Pathology CT Endoscopy Mammograph X-rayPathology X-rayCTFundusUltrasoundEndoscopyMRIPathologyOCT X-rayDermoscopyPathologyFundusUltrasoundOCTCTEndoscopy Figure 12: Demonstration of the collected 2D medical datasets across different modalities and anatomical regions. present but limited in image volume. Conversely, data for other specific regions like the heart or spine is available, highlighting diverse clinical applications. 1) Brain (2 datasets, 220 images). Datasets focused on the brain are represented by two small-scale collections for segmentation tasks: braimMRI (#39) and Brain-MRI (#40), each containing 110 images for analyzing brain tumors and diseases. 2) Abdomen/Pelvis (4 datasets, ∼560 images). This category covers organs such as the colon and prostate. Key tasks include segmentation and classification of tumors in datasets like CMB- CRC (#44) for colorectal cancer and multiple datasets for prostate cancer analysis, including CMB- PCA (#46), Prostate Fused-MRI-Pathology (#48), and Prostate-MRI (#53). These datasets are typi- cally small, with a combined total of around 560 images. 3) Full-Body/Multistructure (8 datasets,∼704,900 images). These datasets provide data from multi- ple anatomical regions/structures, making them suitable for pre-training generalizable models. This category is dominated by RadImageNet (Subset: MR) (#56), a large-scale classification dataset with 673,000 images. Other notable examples include ImageCLEF 2016 (#43) with 31,000 images and multi-organ challenge datasets like the QUBIQ series (#57), (#58). 4) Others (10 datasets,∼16,700 images). This group comprises datasets for various other anatomical regions/structures or those without a specified structure. It includes specialized collections such as Cardiac Atrial Images (#49) for heart segmentation with 8,000 images, SpinalDisease2020 (#41) for spine analysis, and KNOAP2020 (#38) for knee osteoarthritis. It also includes several datasets from The Cancer Imaging Archive where the specific structure is not listed, such as APOLLO-5 (#50) and ICDC-Glioma (GLIOMA01)_3D-MR (#47). MRI Datasets by Tasks. The distribution of datasets is heavily skewed towards classification, which accounts for the vast majority of images due to one large-scale collection. Segmentation is the next most common task, though the corresponding datasets are significantly smaller. A number of datasets are provided without a specific task, offering resources for various research purposes. 1) Classification (3 datasets, 704,000 images). Classification is the most represented task by im- age volume, dominated by RadImageNet (Subset: MR) (#56) (673,000 images) and ImageCLEF 18 2016 (#43) (31,000 images). ImageCLEF 2015 (#55) also falls into this category, although it con- tains no images. 2) Segmentation (6 datasets,∼8,900 images). Segmentation datasets are more numerous but contain far fewer images in total. They cover various organs, including the heart in Cardiac Atrial Im- ages (#49) (8,000 images), the brain in braimMRI (#39) and Brain-MRI (#40), and multiple abdom- inal organs in the QUBIQ challenges (#57), (#58). These datasets often require precise delineation of soft tissues with subtle intensity differences, a task for which MRI is uniquely suited. 3) Multi-task datasets (2 datasets,∼500 images). A couple of small datasets are designed for mul- tiple tasks. CMB-CRC (#44) provides data for both segmentation and classification of colorectal cancer, while CMB-PCA (#46) is designed for classification and prediction in prostate cancer. 4) Others (13 datasets, ∼9,000 images). The remaining 13 datasets cover a range of other tasks or have no specified task. This includes SpinalDisease2020 (#41) for detection (150 images), KNOAP2020 (#38) and CMB-MML (#45) for prediction, and AREN0534 (#37) for estimation (239 images). A significant number of datasets (9) are provided without an explicit task, such as APOLLO-5 (#50) and the ICDC-Glioma series (#47), making them flexible resources for ex- ploratory research. 3.4 PET Slices PET is a functional imaging modality that visualizes metabolic processes, often by tracking the up- take of a radioactive tracer. 2D PET slices are typically used in conjunction with anatomical imaging like CT or MRI for accurate localization of metabolic activity. Therefore, a common characteris- tic of PET datasets is their multi-modal nature (PET/CT or PET/MR). The primary tasks involve detecting and quantifying regions of high metabolic activity, which are often indicative of cancer, inflammation, or neurological disorders. We have collected 13 PET imaging datasets, a majority of which are sourced from The Cancer Imaging Archive (TCIA), as detailed in Table 9. These col- lections often include multiple modalities alongside PET. Compared to CT and MRI datasets, they span less diverse tasks and anatomic regions, focusing primarily on brain and abdominal imaging for segmentation and classification tasks. In total, these datasets comprise approximately 41,942 images. The scale varies significantly, from small collections like CMB-GEC (#62) with only 14 images to the large-scale ImageCLEF 2016 (#60) dataset, which contains 31,000 images. PET Datasets by Anatomical Regions/Structures. The distribution of PET datasets shows a concentration in specific anatomical areas, with a significant number of datasets lacking explicit structural information. Datasets with multi-structure or full-body scope contribute the largest vol- ume of images, primarily due to one large collection. 1) Brain (2 datasets,∼269 images). Brain-related PET datasets are represented by CMB-GEC (#62) and CMB-MEL (#63). These datasets focus on the detection and segmentation of cerebral microb- leeds in melanoma patients. However, their small sample sizes limit their suitability for training large-scale deep learning models. 2) Abdomen/Pelvis (1 dataset, 472 images).This category contains a single dataset, CMB- CRC (#61), which provides images of the colon for research on colorectal cancer. The limited size of this collection may constrain its use for developing complex models. 3) Full-Body/Multistructure (2 datasets,∼31,200 images). This category is dominated by the large- scale ImageCLEF 2016 dataset (#60), containing 31,000 images across skin, cell, and breast struc- tures. The other dataset, AREN0534 (#59), provides 239 images of the kidney and lung. 4) Others (8 datasets,∼10,000 images). The majority of the collected PET datasets do not specify an anatomical region. This category includes collections for various diseases, such as AREN0532 (#69) for Wilms Tumor research. While diverse, many of these datasets, such as CMB-MML (#64) (60 im- ages), have limited numbers of images. This category also includes larger collections like APOLLO- 5 (#66) with 6,200 images. PET Datasets by Tasks. The tasks are unevenly distributed, with classification datasets providing the vast majority of images. A significant number of datasets lack explicit task labels, making them candidates for unsupervised or semi-supervised learning approaches. 19 1) Classification (1 dataset, 31,000 images). The classification task is represented by a single, large- scale dataset, ImageCLEF 2016 (#60), which contains 31,000 images and is designed for classifica- tion challenges. 2) Segmentation (1 dataset, 255 images). The sole dataset dedicated purely to segmentation is CMB- MEL (#63), which provides 255 images for melanoma-related cerebral microbleed segmentation. 3) Multi-task datasets (2 datasets, 486 images). Two small datasets are designed for multiple tasks. CMB-CRC (#61) (472 images) supports both segmentation and classification for colorectal can- cer, while CMB-GEC (#62) (14 images) is annotated for the same tasks in the context of cerebral microbleeds. 4) Others (9 datasets,∼10,200 images). The remaining nine datasets are intended for other specific tasks or have no defined task (’NA’). This group includes AREN0534 (#59) for estimation and CMB- MML (#64) for prediction. The majority, however, are general-purpose collections without specified tasks, such as APOLLO-5 (#66) and AREN0532 (#69), which can be valuable for developing and testing unsupervised models or for a variety of bespoke research questions. 3.5 Ultrasound (US) Images Ultrasound imaging is a real-time, non-invasive, and portable modality, making it widely used for various applications from fetal monitoring to cardiac assessment. A key characteristic of ultra- sound datasets is the inherent image noise (speckle) and operator-dependent variability, which pose significant challenges for automated analysis. Common tasks include segmentation of anatomical structures (e.g., cardiac chambers, fetal head) and classification of lesions (e.g., benign vs. malignant breast tumors). As presented in Table 10, we have collected 19 major ultrasound imaging datasets from various sources including TCIA and Kaggle. The datasets include approximately 457,663 images in total, with RadImageNet-US (#76) contributing the vast majority (390k images). Ultrasound Datasets by Anatomical Regions/Structures. The available datasets cover a wider range of anatomical regions/structures including the skull, breast, heart, thyroid, and liver, in addi- tion to full-body imaging, though some TCIA collections (APOLLO-5 (#72) and CMB-LCA (#608)) lack anatomic specifications. Following the guideline that datasets containing multiple organs are categorized separately, RadImageNet-US (#76) represents the most comprehensive full-body cover- age with 390k images, while other datasets remain relatively small-scale. 1) Breast (2 dataset, ∼803 images). The BUSI (#71) and BreastMNIST (#77) datasets focus on breast ultrasound for cancer detection, providing segmented images with binary classification labels. This small-scale collection may support basic supervised learning applications. 2) Skull (1 dataset, 1,344 images). HC18 (#70) targets fetal head circumference measurement through skull ultrasound imaging. As a challenge dataset with C BY 4.0 license, it facilitates standardized benchmarking. 3) Full-Body (1 dataset, 390k images). RadImageNet-US (#76) dominates the ultrasound category with extensive coverage of 15 abdominal structures, though its commercial license may restrict accessibility. 4) Multi-structure (2 datasets, 31,239 images). Two datasets, including ImageCLEF 2016 (#75) and AREN0534 (#78), cover multiple structures such as skin, breast, kidney, and lung. 5) Others (12 datasets,∼31,200 images). The remaining datasets focus on specific organs like the heart (CAMUS (#89)), thyroid (TN-SCUI2020 (#81)), and brachial plexus (Ultrasound Nerve Seg- mentation (#80)), or lack detailed anatomic descriptions. These multi-modal collections currently provide 6,203 images from APOLLO-5 (#72), while CMB-LCA (#608) has no available images. Ultrasound Datasets by Tasks. Ultrasound datasets show several distinct task types represented, namely measurement, segmentation, and classification, along with tracking, estimation, and recon- struction. Among classification datasets, RadImageNet (US) (#76) has the largest image count, while ImageCLEF 2016 (#75) offers more classes (30). 1) Classification (5 dataset, ∼421,500 images). RadImageNet-US (#76) offers large-scale multi- class classification across 15 abdominal categories. 20 2) Segmentation (8 dataset, ∼26,300 images). Multiple datasets including BUSI (#71), CA- MUS (#89), and the Ultrasound Nerve Segmentation (#80) dataset provide pixel-level annotations for organ and tumor segmentation, supporting computer-aided diagnosis development. The main challenge in these datasets is dealing with weak boundaries and acoustic shadowing artifacts. 3) Measurement (1 dataset, 1,300 images). HC18 (#70) specializes in biometric measurement tasks, particularly fetal head circumference calculation. 4) Unlabeled datasets (3 datasets). The TCIA datasets (APOLLO-5 (#72), CMB-LCA (#608), and AREN0532 (#87)) currently lack labels; APOLLO-5 (#72) contains 6,203 images and AREN0532 (#87) contains 1,021 images, while CMB-LCA (#608) has none available, though their multi-modal nature may enable future fusion studies. 3.6 X-Ray Images As one of the oldest and most common medical imaging techniques, 2D X-ray (radiography) pro- vides a projectional view of anatomical structures, excelling at visualizing bone and air-filled spaces like the lungs. X-ray datasets are characterized by their large volume, particularly for chest imaging, driven by routine screening for diseases like pneumonia and tuberculosis. The primary tasks are classification of pathologies and segmentation or localization of abnormalities, though the overlap- ping of anatomical structures in the 2D projection can make these tasks challenging. Table 11 shows the 61 major X-ray imaging datasets from diverse sources, including TCIA, Grand Challenges, and open data platforms. These collections comprise approximately 1,657,000 images in total. The CheXmask (#143) dataset dominates the quantity with 676,800 images for lung segmentation, fol- lowed by the CheXpert (#114) and VICTRE (#139) datasets, while most other datasets range from hundreds to thousands of samples, presenting a long-tail distribution common in medical imaging. X-Ray Datasets by Anatomical Regions/Structures. The collected X-ray datasets cover diverse anatomical regions, with a strong emphasis on thoracic imaging due to its clinical prevalence in pulmonary and cardiac diagnostics. Approximately 46% of the datasets focus on the chest/lung region, reflecting the widespread use of X-rays for respiratory disease screening (e.g., COVID- 19, pneumonia). Other anatomical regions/structures are less represented, with limited datasets for musculoskeletal, neurological, and abdominal applications. 1) Thorax/Lung (28 datasets,∼537,900 images). This category dominates the X-ray collections, in- cluding large-scale datasets like NIH Chest X-ray 14 (#96) (112,100 images) and CheXpert (#114) (224,300 images). These datasets are notable for their multi-label classification tasks, where a sin- gle image can be associated with multiple pathologies. The ChestX-Det (#121) series (3,600 im- ages) provides detailed annotations for lung pathologies, while MIDRC-RICORD-1c (#128) (1,300 images) supports COVID-19 research. Smaller datasets like JSRT (#124) (247 images) focus on pneumonia and pulmonary nodules. Breast / Mammography (3 dataset, ∼248,300 images). VICTRE (#139) dominates this category. VICTRE’s (#139) massive scale underscores breast imaging’s importance but lacks disease annota- tions. Mammography datasets are characterized by the need to detect subtle signs of cancer, such as microcalcifications and masses, in dense breast tissue. 2) Musculoskeletal (8 datasets, ∼15,681 images).Musculoskeletal datasets include spine (AASCE (#105), 609 images), clavicle (CRASS (#119), 518 images), and pelvic bone (PENGWIN2024-Task2 (#148), 150 images) studies. The TCB-Challenge (#118) (174 images) targets osteoporosis detection via bone radiographs, highlighting X-ray’s role in orthopedic diag- nostics. A common task in these datasets is fracture detection and classification. 3) Brain/Head (2 datasets, ∼1,400 images). Brain datasets are limited to DENTEX (#133) (1,000 images) for dental imaging and Cephalometric X-ray Image (#126) (400 images) for cephalometric analysis, indicating a gap in neurological X-ray datasets compared to CT/MRI. 4) Multi-structure (5 datasets, ∼186,200 images). This category includes datasets spanning multi- ple distinct anatomical regions, such as MedMNIST (#111) (100,000 images) and MURA (#108) (40,000 images). 21 5) Others (8 datasets, ∼700,000 images). Includes generic collections like the CheXmask (#143) (676,800 images) and X-ray Pneumonia Image Dataset (#94) (5,900 images) without detailed anatomic labels. X-Ray Datasets by Tasks. The datasets exhibit clear task specialization, with classification being the most prevalent application scenario. Notably, 31% of the collections (19/61) provide pixel-level annotations or detection labels, reflecting the clinical demand for precise localization in diagnostic imaging. 1) Classification (30 datasets, ∼670,100 images). This category represents the largest task group, predominantly focusing on pulmonary and COVID-19 related diagnoses. Key collections include CheXpert (#114) (224,300 images), NIH Chest X-ray 14 (#96) (112,100 images), and RANZCR CLiP (#122) (30,100 images, catheter classification). The JSRT (#124) dataset, though small (247 images), provides valuable multi-class annotations for both pneumonia and pulmonary nodules. 2) Segmentation (10 datasets,∼708,500 images). These datasets emphasize anatomical structure de- lineation, with CheXmask (#143) (676,800 images) and Pneumothorax Masks X-Ray (#98) (12,000 images) being the most substantial. The Pulmonary Chest X-Ray (#132) dataset (800 images) specif- ically targets lung abnormality segmentation, while CRASS (#119) (518 images) focuses on clavicle identification for orthopedic applications. 3) Detection/Localization (9 datasets,∼59,700 images). Emerging needs for surgical planning are addressed by DENTEX (#133) (1,005 brain images) and CL-Detection2023 (#134) (555 images). The CEPHA29 (#135) dataset (1,000 images) stands out for cephalometric landmark localization, despite its current data accessibility issues. 4) Others (5 datasets, ∼36,100 images). Unique applications include AASCE’s (#105) spinal curvature regression (609 images), CoronARe’s (#138) vascular reconstruction, and RSNA Bone Age’s (#146) bone age estimation (14,200 images). These demonstrate X-ray’s versatility beyond conventional diagnostic roles. 3.7 Optical Coherence Tomography (OCT) Images OCT provides micrometer-resolution, cross-sectional images of biological tissues in real-time. It is analogous to "optical ultrasound," using light instead of sound. Its primary application is in ophthalmology for imaging the layers of the retina. Consequently, OCT datasets are highly spe- cialized, focusing on tasks like retinal layer segmentation for thickness mapping and classification of retinal diseases based on layer morphology. Table 12 provides 22 major optical coherence to- mography (OCT) imaging datasets from diverse sources, including Kaggle, Grand Challenges, and academic institutions. These collections demonstrate remarkable specialization in retinal imaging, comprising approximately 221k images in total. Two large public classification benchmarks — OCT2017 (#150) (about 83.5k images) and MedMNIST (#158) (100k images) — account for the majority of images in the corpus. In contrast, most other datasets range from hundreds to thousands of samples, presenting a typical long-tail distribution in medical imaging resources. OCT Datasets by Anatomical Regions/Structures. Notably, almost all of the datasets focus ex- clusively on retinal applications, reflecting OCT’s primary clinical use in ophthalmology. The only exception is MedMNIST (#158), which can also be applied to breast and lung. As such, we do not break down to introduce the anatomical regions/structures. OCT Datasets by Tasks. The datasets exhibit clear task specialization, with classification and segmentation being the most prevalent application scenarios. The classification task has the largest number of images, though the number of datasets for classification is less than that of the seg- mentation task. Segmentation datasets account for approximately 50% of the datasets, providing pixel-level annotations for precise anatomical analysis. 1) Classification (5 datasets, ∼210,200 images). This category represents the largest task group in terms of image number, predominantly focusing on diabetic retinopathy and glaucoma detec- tion. Key collections include OCT2017 (#150) (83,484 images), Retinal OCT-C8 (#151) (24,000 images), and MedMNIST (#158) (100,000 images combining multiple modalities). The core task in these datasets is to distinguish diseases based on morphological changes in retinal layers, such as 22 the presence of drusen or intraretinal fluid. The iChallenge-AGE19 (#152) dataset (1,600 images) specifically targets glaucoma classification with detailed angle closure annotations. 2) Segmentation (11 datasets,∼2,600 images). These datasets emphasize retinal layer delineation, with SinaFarsiu-009 (#163) (840 images) and SinaFarsiu-018 (#167) (784 images) providing the most substantial annotations. The DRAC22 (#153) dataset (174 images) specializes in diabetic retinopathy lesion segmentation, while iChallenge-GOALS (#154) (300 images) offers three-layer retinal segmentation crucial for thickness measurements. 3) Prediction (3 datasets,∼8,500 images). The APTOS series (APTOS-2021 (#156), APTOS Cross- Country Stage 1 (#157), and APTOS Cross-Country Stage 2 (#168)) total 8,500 images for diabetic retinopathy severity prediction, using the International Clinical Diabetic Retinopathy scale. These datasets demonstrate OCT’s growing role in quantitative disease progression monitoring. 3.8 Fundus Images Fundus photography captures high-resolution color images of the retina, making it a cornerstone of ophthalmology. A key characteristic of fundus datasets is their similarity to natural RGB images in terms of data format, which allows for the direct application and transfer learning of models de- veloped for general computer vision. However, the content is highly specialized, featuring unique anatomical landmarks like the optic disc, fovea, and a complex network of blood vessels. Common tasks revolve around detecting and grading pathologies such as diabetic retinopathy and glaucoma. The challenge lies in identifying these subtle, often minute, pathological features within a com- plex anatomical background. Table 13 shows 75 major fundus photography datasets from diverse sources, including Grand Challenges, Kaggle, and academic institutions. These collections demon- strate remarkable specialization in retinal imaging, comprising approximately 412,400 images in total. The AIROGS (#182) dataset dominates the quantity with 101,400 images, while most other datasets range from hundreds to thousands of samples, presenting a typical long-tail distribution in medical imaging resources. Notably, almost all of the datasets focus exclusively on retinal applica- tions, reflecting fundus photography’s primary clinical use in ophthalmology diagnostics. Fundus Photography Datasets by Anatomical Regions/Structures. The collected datasets ex- clusively focus on retinal imaging, reflecting fundus photography’s specialized application in oph- thalmology. All 75 datasets target the retina, with varying emphasis on specific anatomical structures or pathological features. This extreme specialization contrasts with other modalities like CT or MRI that cover multiple body regions. Fundus Photography Datasets by Tasks. The datasets exhibit clear task specialization, with clas- sification being the most prevalent application scenario. Approximately 30% of the collections provide pixel-level annotations or detection labels, enabling precise anatomical analysis crucial for diagnostic applications. 1) Classification (42 datasets, ∼304,200 images). This category represents the largest task group, predominantly focusing on diabetic retinopathy and glaucoma detection. Key collections include OIA-ODIR (#214) (10,000 images), APTOS 2019 (#194) (5,590 images for diabetic retinopathy grading), and Yangxi (#200) (20,394 images for eye axis classification). These datasets are pivotal for developing automated screening systems for prevalent eye diseases, often framed as multi-class grading problems based on the number and type of lesions present. The JSIEC (#207) dataset (1,000 images) stands out for its comprehensive coverage of 38 fundus disease categories, though sample sizes per category remain limited. 2) Segmentation (21 datasets, ∼5,300 images). These datasets emphasize retinal structure delin- eation, with RIM-ONE (#193) (485 images) and GAMMA CFP (#240) (200 images) providing optic disc/cup annotations crucial for glaucoma assessment. The HRF Seg (#211) dataset (45 im- ages) offers high-resolution vessel segmentation, while AO-SLO (#197) (840 images) specializes in photoreceptor mapping. The iChallenge-GAMMA series ((#241), (#218)) demonstrates growing interest in multi-modal retinal analysis. Segmentation tasks are critical for quantitative analysis, focusing on delineating blood vessels to assess vascular health, the optic disc and cup to measure glaucomatous changes, and lesions like exudates or hemorrhages to quantify disease severity. 23 3) Regression (6 datasets,∼2,300 images). The INSPIRE series ((#208), (#209)) (70 images com- bined) focuses on arteriovenous ratio measurement, while DeepDR-Task2 (#219) (2,000 images) addresses disease progression prediction. These datasets highlight fundus photography’s expanding role in quantitative disease monitoring. 3.9 Dermoscopy Images Dermoscopy involves imaging the skin with a specialized magnifying lens to visualize subsurface structures not visible to the naked eye. These datasets are crucial for the early detection of skin cancer, particularly melanoma. The images are typically high-resolution RGB photos of skin le- sions. Key tasks include the segmentation of lesion boundaries and the classification of lesions into categories (e.g., benign nevus, melanoma, basal cell carcinoma). There are 17 major dermoscopy imaging datasets in our collection, as shown in Table 14. They are collected from various sources, in- cluding ISIC challenges, CVPR competitions, and independent research collections. These datasets predominantly focus on skin imaging. They primarily address segmentation and classification tasks, with a strong emphasis on skin lesion analysis. These datasets include approximately 167,300 images in total, with Monkeypox (#255) having the largest single collection (40,200 images) and ISIC20 (#243) (33,100 images), ISIC19 (#247) (25,300 images), and Fitzpatrick17k (#248) (16,600 images) also providing substantial sample sizes for training medical imaging models. Dermoscopy Datasets by Anatomical Regions/Structures. The vast majority of these datasets focus on skin imaging, though a few cover other anatomical regions. 1) Skin (13 datasets,∼133,600 images). This dominant category includes all ISIC challenge datasets (ISIC16-20 (#??)), Fitz- patrick17k (#248), MED-NODE (#249), PH2 (#251), and others. The largest collections are Mon- keypox (#255) (40,200 images), ISIC20 (#243) (33,100 images), and ISIC19 (#247) (25,300 im- ages). These datasets demonstrate strong clinical focus on melanoma detection and skin lesion analysis. 2) Foot (1 dataset, 2,000 images). DFUC2020 (#252) specifically targets foot imaging for diabetic foot ulcer analysis. 3) Thyroid (1 dataset, 637 images). DDTI focuses on thyroid nodule segmentation. 4) Multi-structure (1 datasets, ∼31,000 images). ImageCLEF2016 (#254) covers skin, cell, and breast imaging with 31,000 images. Dermoscopy Datasets by Tasks. The collected datasets show clear task specialization, with most providing high-quality labels suitable for supervised learning. 1) Segmentation (5 datasets,∼9,400 images). Key collections include ISIC16 (#244) (1,279 images), ISIC17 (#245) (2,750 images), ISIC18 (#242) (2,694 images), and DDTI (637 images). These typically focus on precise lesion boundary delineation. 2) Classification (10 datasets,∼157,500 images). Major collections include Monkeypox (#255) (40,200 images), ISIC20 (#243) (33,100 images), ImageCLEF 2016 (#254) (31,000 images), and ISIC19 (#247) (25,300 images). These datasets often provide multi-class categorization of skin lesions. Unlabeled dataset (1 dataset, 368 images). Vitiligo (#256) is the only unlabeled collection, potentially useful for unsupervised learning. 3.10 Histopathology Histopathology is the microscopic examination of tissues to study the manifestations of disease. Digital pathology datasets, particularly those based on Whole Slide Images (WSIs), possess unique characteristics. WSIs are gigapixel-resolution images, often exceeding 100,000×100,000 pixels, which makes it computationally infeasible to process them directly. Consequently, a standard pre- processing pipeline involves patch extraction or tiling, where the WSI is divided into thousands of smaller, manageable patches. Common tasks include patch-level classification (e.g., identifying tu- morous vs. normal tissue), object-level segmentation or detection (e.g., delineating nuclei, glands, or mitotic figures), and WSI-level classification for diagnosis. The challenges in this modality stem from the massive image size, significant variations in staining and preparation, and the need to ag- gregate patch-level predictions into a coherent slide-level diagnosis. Tables 15 and 16 present 117 major histopathology imaging datasets from diverse sources, including grand challenges (MICCAI, ISBI), open data platforms (TCGA, TCIA, OpenDataLab), and research collections. These datasets predominantly utilize hematoxylin and eosin (H&E) staining, with some incorporating immunohis- tochemistry (IHC). They collectively contain approximately 2.22 million images (comprising∼2.15 million patch images and∼67,000 WSI), with the Quilt-1M (#371) (1,000,000 images) and Patch- Camelyon (PCam) (#345) (328,000 images) being the largest collections. Notably, 82% of datasets 24 provide high-quality labels suitable for supervised learning. The prohibitive cost of large-scale WSI annotation catalyzed a shift towards SSL, enabling the rise of Pathology Foundation Models from vast unlabeled data archives. Initial development centered on algorithmic innovations using public datasets like TCGA. A subsequent "scale revolution" utilized massive, private "real-world" datasets, powering models like UNI (trained on over 100,000 WSIs) and Prov-GigaPath (trained on over 171,000 WSIs). This addressed the "domain shift" limitations of public data, proving that dataset scale is now a primary engine of progress in the field. Histopathology Datasets by Anatomical Regions/Structures. The datasets show a strong clini- cal focus on cancer diagnosis across multiple anatomical sites. 1) Breast (25 datasets,∼53,000 im- ages). Major collections include BRIGHT (#270) (5,086 images), BRCA-M2C (#304) (120 images), and the BreakHis series (#299, #327, #327, #329) (combined 35,236 images across magnifications). These primarily address tumor classification and segmentation. 2) Prostate (9 datasets, ∼42,000 images). PANDA (#336) (10,616 images) and SICAPv2 (#300) (18,783 images) are the largest, focusing on Gleason grading. 3) Colon/Rectum (12 datasets,∼113,000 images). CRC100K (#303) (100,000 images) and CoNIC2022 (#271) (4,981 images) provide extensive data for colorectal can- cer analysis. 4) Multi-organ (17 datasets,∼1.18 million images). Quilt-1M (#371) (1,000,000 im- ages) and MedMNIST (#276) (100,000 images) cover multiple cancer types. 5) Others include lung (7 datasets, ∼38,000 images), lymph nodes (9 datasets, ∼537,000 images), and blood (5 datasets, ∼53,000 images). Histopathology Datasets by Tasks. The datasets demonstrate specialized task distributions. Emerging trends include increased WSI adoption (32% of recent datasets) and multi-task collec- tions combining segmentation with classification or counting. 1) Classification (38 datasets, ∼709,000 images). Key datasets include LC25000 (#297) (25,000 images, lung/colon classification) and Histopathologic Cancer Detection (#277) (220,000 images). The BreakHis series (#299, #327, #328, #329) provides multi-magnification classification (40×- 400×). A key challenge is handling intra-class variation and inter-class similarity at the cellular level, making fine-grained classification difficult. 2) Segmentation (31 datasets,∼368,000 images). Notable collections are GlaS (#294) (165 images, colorectal glands) and CRAG (#306) (213 im- ages, extended from GlaS). Segmentation targets range from macro-structures like tumor regions to micro-structures like individual nuclei or glands, which are essential for quantitative pathology. 3) Detection (6 datasets,∼14,000 images). MIDOG2021 (#281) (200 images) focuses on mitotic figure detection. 4) Multi-task (4 datasets, ∼14,000 images). PanNuke combines segmentation and clas- sification (PanNuke (Seg) (#298), 7,901 images), while CoNIC2022 (#271) adds counting tasks. 5) Specialized tasks include registration (ANHIR (#266), 481 images), generation (BCI (#285), 4,900 images) and VQA (Quilt-1M (#371), 1,000,000 images). 3.11 Microscopy Imaging Table 17 summarizes 34 major microscopy imaging datasets. These datasets predominantly utilize brightfield and fluorescence microscopy, with a strong focus on cellular and subcellular imaging. They collectively contain approximately 1.8 million images, with the CellTracking2019 (#372) dataset (1.44 million images), DLBCL-Morph (#391) (152,200 images), and Kaggle-HPA (#385) (89,460 images) being the largest collections. Unlike histopathology which focuses on tissue archi- tecture, these microscopy datasets often center on the morphology, count, and behavior of individual cells or microorganisms. Notably, most datasets provide high-quality labels suitable for supervised learning, covering a wide range of biological scales from single molecules to whole organisms. Microscopy Datasets by Anatomical Regions/Structures. The datasets demonstrate specialized focus on specific anatomical structures: 1) Cellular (8 datasets, ∼1.51M images). Key collections include CellTracking2019 (#372) (16,042 sequences, 1.44M frames), Kaggle-HPA (#385) (89,460 images), and OCCISC ((#381), (#403)) (945 images). These primarily address cell segmentation and tracking. 2) Ocular (5 datasets, ∼153,000 images). The corneal series (CornealNerve (#388), NerveTortuosity (#389), CornealEndothelial (#387)) and DLBCL-Morph (#391) (152,200 images) focus on eye microstructure analysis. 3) Breast (1 dataset, 400 images). ICIAR2018 (#382) pro- vides histopathology images for breast cancer classification. 4) Blood (3 datasets, ∼28,500 im- ages). Blood Cell Images (#375) (12,500 images) and Leukemia Classification (#376) (15,100 25 images) analyze blood cell morphology. 5) Multi-structure (2 datasets, ∼31,500 images). Image- CLEF2016 (#399) (31,000 images) covers multiple tissue types. Microscopy Datasets by Tasks. There is an increased use of deep learning benchmarks (Kaggle- HPA (#385)) and integration of multiple tasks (CBC series (#??) combining counting and detection). The datasets show clear specialization in analysis tasks: 1) Segmentation (11 datasets, ∼99,000 images). Kaggle-HPA (#385) (89,500 images), CREMI (#373), and OCCISC-Seg (#381) (945 im- ages) provide precise cellular boundary delineation. A common challenge is accurately separating densely clustered or overlapping cells. 2) Classification (12 datasets,∼81,400 images). ImageCLEF 2016 (#399) (31,000 images), B-ALL Classification (#378) (15,100 images), and ICIAR2018 (#382) (400 images) enable morphological categorization. 3) Detection (3 datasets, ∼2,600 images). BloodCell (#395) (874 images) and Tuberculosis (#396) (1,265 images) localize specific cellular features. 4) Tracking (1 datasets,∼1.4M images). CellTracking2019 (#372) dominates this category with 1.4 million time-lapse frames. 5) Specialized tasks include regression (DLBCL-Morph (#391), 152.2k images; CBC-Count (#383), 420 images) and protein localization (Kaggle-HPA (#385)). 3.12 Infrared Imaging Infrared imaging in medicine captures thermal patterns or reflectance properties not visible in the normal spectrum. In the context of the collected datasets, it is primarily used in ophthalmology to image retinal structures with different light wavelengths. This modality is non-invasive and can pro- vide unique contrast for features like the retinal pigment epithelium. The tasks often revolve around image quality assessment or classification based on specific features visible in the infrared spectrum. Table 18 includes 6 major infrared reflectance imaging datasets. These collections focus exclusively on ocular imaging, particularly retinal analysis, using infrared reflectance technology. The datasets contain approximately 424,532 images in total, with the MRL Eye series ((#??)) (combined 424,490 images across 5 sub-datasets) representing the largest collection. All datasets provide high-quality labels suitable for supervised learning, with a strong emphasis on classification tasks (5/6 datasets). Infrared Datasets by Anatomical Regions/Structures. Infrared imaging remains highly special- ized, with 100% of datasets focusing on retinal applications, and all created since 2018, suggesting growing interest in this modality. Specifically, Retina (six datasets, ∼424,532 images). The MRL Eye series ((#??)) (84,898 images per sub-dataset) provides comprehensive coverage of various retinal features. This extreme specialization in retinal imaging contrasts with other modalities that typically cover multiple anatomical regions. Infrared Datasets by Tasks. The datasets show clear task specialization: 1) Classification (5 datasets, ∼424,490 images). The MRL Eye series addresses multiple classification tasks: glasses detection (MRL-Eye-Glasses (#407)), eye state (MRL-Eye-State (#408)), reflection analysis (MRL- Eye-Reflections (#409)), image quality assessment (MRL-Eye-Quality (#410)), and sensor type identification (MRL-Eye-Sensor (#411)). 2) Segmentation (one dataset, 42 images). RAVIR (#406) is the only segmentation dataset, focusing on retinal blood vessel delineation with three classes (background, arteries, veins). 3.13 Endoscopy Imaging Endoscopy provides direct real-time video visualization of internal organs and cavities through a flexible tube with a camera. Datasets are often composed of individual frames extracted from these videos. A key characteristic is the high variability in appearance due to camera motion, lighting changes, specularity, and physiological artifacts (e.g., bubbles, debris). Common tasks include polyp detection and segmentation for cancer screening, tool tracking for surgical navigation, and classification of tissue abnormalities. We provide an overview of endoscopy imaging datasets in Table 19, where 41 major ones are collected from diverse sources, e.g., ISBI and MICCAI. These datasets predominantly feature endoscopic imaging (39/41), with a few incorporating multi-modal data (2/41). They cover diverse anatomical regions and tasks, totaling approximately 322,200 im- ages and videos, with EndoSlam (#413) being the largest collection (76,837 images). Notably, 39% of datasets (16/41) contain over 1,000 images, making them potentially suitable for training medical vision models. 26 Endoscopy Datasets by Anatomical Regions/Structures The datasets cover several major anatomical regions, with strong emphasis on gastrointestinal tract examination: 1) Colon/Bowel (8 datasets, ∼109,400 images): This represents the most extensively examined region, featuring large-scale datasets like SUN_SEG (#428) (49,136 images), SARAS-ESAD (#425) (33,398 images), and Kavsir (#412) (14,000 images) for polyp segmentation and detection. The CVC series (CVC-ClinicDB (#422), CVC-ColonDB) provide high-quality annotations for polyps, while EndoCV2020 (#416) and EndoVis15 (#417) focus on artifact detection. 2) Esophagus (1 datasets, 157 images): Focused on Barrett’s esophagus detection, with AIDA- E_2 (#420) (157 images) providing a specialized benchmark. 3) Multi-structure gastrointestinal tract (6 datasets, ∼86,000 images): Comprehensive collections like EndoSlam (#413) (76k images) cover the entire gastrointestinal tract including esophagus, stom- ach, and colon. These are particularly valuable for developing generalizable endoscopic AI systems. 5) Other Regions: Includes specialized collections for uterus (FetReg (#424), 2.7k images), gallblad- der (m2cai16-tool (#418), 15 videos), and prostate (SARAS-MESAD (#414), 50k images). While clinically important, these generally have smaller sample sizes. Endoscopy Datasets by Tasks The datasets demonstrate a progression from single-task to multi- task benchmarks: Segmentation (17 datasets,∼20,000 images): Forms a large task category, with Kvasir-SEG (#423) (8,000 images), FetReg (#424) (2,718 images), and EndoVis 2018 - RSS (#436) (2,840 images) providing high-quality segmentation masks. Most focus on polyp segmentation, while specialized targets include surgical tools (EndoVis 2018-RSS (#436)) and placental vasculature (FetReg (#424)). Detection (6 datasets, ∼86,600 images): SARAS-MESAD (#414) (50,284 images) and SARAS- ESAD (#425) (33,398 images) are notable for bounding box annotations of abnormalities and in- struments. The m2cai series (#418) provide instrument detection benchmarks. Classification (10 datasets, ∼77,700 images): Ranges from binary classification (MedFM2023) to fine-grained categorization (ImageCLEF (#426)). AIDA series (E1-E3) (#??) provide histology classification benchmarks. Multi-task datasets (5 datasets, 156k images): HyperKvasir (#429) (captioning, classification, local- ization), SUN_SEG (#428) (segmentation, detection, classification), and Endo-FM (#445) combine multiple annotation types, reflecting recent trends towards comprehensive benchmarks. Others:Includes reconstruction and depth estimation (EndoSlam (#413)) and registration (P2ILF (#441)). Some of these tasks, like in the EndoSlam (#413) dataset (76,837 images), are supported by a large number of samples. 3.14 Other Modalities Finally, we introduce all the 2D datasets of other modalities that are not listed in the previous sub- sections. This section consolidates datasets from a variety of imaging modalities that, while less numerous than the major categories, represent important and often specialized clinical applications. Table 20 summarize the information of these modalities, spanning diverse modalities, including Mammography (4 datasets), X-Ray (3), Fundus (2), Colposcopy (2), and others. These datasets col- lectively contain approximately 858,000 images, with the Digital Mammography (#451) dataset be- ing the largest (640,000 images), followed by MRL Eye Gender (#462) (84,898 images) and ADDI ALZHEIMER’S DETECTION CHALLENGE (#450) (34,614 images). The datasets demonstrate a strong emphasis on classification tasks (75%) and cover all major anatomical regions, though with uneven distribution across modalities. Datasets by Anatomical Regions/Structures. The datasets cover comprehensive anatomical structures with a particular concentration on thoracic and retinal imaging. 1) Thoracic/Lung (2 datasets, ∼27,000 images). This category includes collections like VinDr-CXR (#467) (18,000 images) and VinDr-PCXR (#466) (9,125 images) for lung abnormalities. 2) Retina (3 datasets, ∼88,000 images). Retinal imaging features collections like MRL Eye Gender (#462) (84,898 im- ages) and specialized datasets for various ophthalmic diseases. 3) Breast (4 datasets, ∼663,000 27 images). The Digital Mammography (#451) dataset dominates this category with 640,000 images, supplemented by specialized collections like CMMD (#457) (1,775) and VinDr-Mammo (#464) (19,992). 4) Brain/Head (2 datasets, ∼5,000 images). While smaller in quantity, these include important collections like Br35H (#458) (3,060) for brain tumors. 5) Whole-body/Multi-structure collections like OralCancer (#460) (131 images) provide cross-anatomical coverage. Datasets by Tasks. The datasets demonstrate clear task specialization across modalities. 1) Clas- sification (15 datasets,∼798,000 images): Mammography datasets like The Digital Mammography DREAM Challenge (#451) and retinal collections (MRL Eye Gender (#462)) dominate this cate- gory. 2) Segmentation (4 datasets,∼2,300 images): Notable collections include CDD-CESM (#459) (2,006 images). 3) Multi-task datasets like CDD-CESM (#459) (segmentation+classification) pro- vide versatile training opportunities. 4) Emerging tasks like reconstruction (BigNeuron (#452)) demonstrate expanding research frontiers. 3.15 Challenge and Opportunity The landscape of 2D medical imaging datasets presents a distinct duality. On one hand, its sheer volume, particularly in modalities like histopathology and radiography, offers a scale for model pre- training that is unparalleled in the medical domain. On the other hand, this abundance is coupled with significant fragmentation, heterogeneity, and the inherent limitations of two-dimensional rep- resentations, posing unique challenges for the development of robust and generalizable foundation models. Key Challenges in 2D Medical Imaging Datasets. The primary obstacles stem from the diversity and nature of 2D data acquisition and annotation practices. Extreme fragmentation and heterogeneity represent a major barrier. The vast number of 2D datasets are scattered across numerous indepen- dent repositories and challenges, often with inconsistent imaging protocols, varying resolutions, and non-standardized metadata. This leads to significant domain shifts between datasets of the same modality, complicating large-scale integration efforts. For instance, histopathology slides exhibit wide variations in staining and preparation, while chest X-rays differ in projection and exposure settings. Pervasive data imbalance and long-tail distributions introduce substantial biases. As our analy- sis reveals, modalities like pathology, X-ray, and fundus photography dominate the data landscape, while clinically vital modalities such as endoscopy and ultrasound remain underrepresented. This imbalance extends to anatomical regions and tasks; for example, over 80% of images in our col- lection come from just thoracic and breast datasets, leaving other regions (e.g., abdominal organs) critically underserved. This also creates modality-specific limitations; for instance, X-Ray datasets in this collection average only∼12.5K images per dataset. Foundation models pre-trained on such skewed data may fail to generalize to less common modalities or pathologies, limiting their clinical utility. Furthermore, annotation quality and scalability present a persistent challenge. The creation of large- scale 2D datasets often relies on weak supervision, such as labels extracted from radiology reports, which can be noisy and imprecise. While pixel-level annotations are the gold standard, they are labor-intensive and scarce at scale. The lack of a unified annotation ontology across datasets makes it difficult to harmonize labels for multi-dataset training, hindering the creation of truly comprehensive benchmarks. Finally, the inherent limitation of 2D representation is a fundamental constraint. A single 2D im- age, whether a projection like an X-ray or a slice from a volume, provides only a partial view of the underlying three-dimensional anatomy. This loss of spatial context can be a critical handicap for diagnosing complex diseases that require volumetric understanding, such as assessing tumor morphology or subtle structural changes. Opportunities for Advancement. Despite these challenges, the 2D medical imaging domain of- fers exceptional opportunities to advance foundation models. The unprecedented scale for self- supervised pre-training is the most significant advantage. With millions of available images, thoracic imaging (pathology and chest radiography) has achieved a critical mass for large-scale AI training. This scale, alongside exceptionally standardized large collections (such as the >80K retinal image 28 (a) (b) (c) Figure 13: The distribution of different (a) modalities, (b) anatomical structures, and (c) tasks for 3D datasets. Each slice of the pie chart shows the percentage and the actual number of images. datasets), enables the effective application of self-supervised learning paradigms, such as masked auto-encoding and contrastive learning, to build foundational backbones that can be fine-tuned for a multitude of downstream tasks. The rich diversity of modalities enables powerful multi-modal learning. The breadth of 2D imaging, spanning from macroscopic radiographic images to microscopic pathology slides, provides a fertile ground for developing models that can reason across different biological scales and data sources. A particularly promising avenue is the integration of imaging data with unstructured clinical text. Large datasets paired with radiology reports, such as MIMIC-CXR [63] and CheXpert [64], unlock the potential for vision-language pre-training, allowing models to learn semantically rich represen- tations that align visual features with clinical narratives. Moreover, the widespread clinical use and lower cost of 2D imaging modalities create opportunities for high-impact, scalable clinical applications. Foundation models trained on common 2D data like X-rays, fundus, or dermoscopy images can be deployed for large-scale screening programs in resource-constrained settings. This can democratize access to expert-level diagnostics for conditions like tuberculosis, diabetic retinopathy, and skin cancer, addressing critical global health challenges. In summary, while the path to building generalist 2D medical foundation models is fraught with challenges of data heterogeneity and annotation quality, the opportunities are immense. Strategic dataset consolidation, prioritization of balanced anatomical coverage, and the development of stan- dardized multi-task annotations, coupled with advanced self-supervised and multi-modal learning techniques, can harness the vast scale of 2D data to create transformative AI tools for global health- care. 4 3D Medical Image Datasets We have collected 591 3D medical image datasets, comprising 1,242,022+ volumes in total. Al- though the total number of volumes is considerably smaller than that of 2D datasets, 3D datasets provide richer spatial information that is essential for volumetric analysis and clinical decision- making. We categorize these 3D datasets according to their modalities, tasks, and body parts. The labeled datasets dominate the collection, while unlabeled datasets provide additional opportunities for self-supervised learning approaches. 4.1 Overview We first provide an overview of 3D medical image datasets. Figure 13 shows the distributions of different modalities, anatomical structures, and tasks for 3D datasets, which represent clear long-tail distributions. In terms of modality, MRI and CT are the most popular, while other modalities, like PET, ultrasound, and OCT, are less representative. From the perspective of anatomical structures, the brain, abdomen, and lung have the largest number of datasets, while the prostate, teeth, and other structures are still limited in their dataset numbers. The dominating tasks include classification, segmentation, and other tasks. However, other tasks, e.g., registration, localization, and detection, have much fewer datasets. Figure 14 demonstrates representative examples of the collected 3D medical image datasets across different modalities and anatomical regions. 29 Figure 14: 3D visualization examples of medical imaging datasets across different modalities and anatomical structures. 4.2 CT Volumes CT is a widely used imaging modality that employs X-rays to visualize internal structures in three dimensions. We identify 252 3D CT datasets comprising approximately 516,087 volumes in total, as summarized in Table 21. These datasets diverge considerably in scale and annotation quality, from small, domain-specific collections (for example, 3D-IRCADb [65] with 20 liver volumes) to large, multi-center compilations such as CT-RATE [36] (50,188 volumes). Large collections like CT-RATE and M3D [66] aim to cover a wide range of acquisition protocols but often depend on semi-automated or weak supervision for annotations, while curated challenge datasets like TotalSeg- mentator [67] (1,204 volumes) deliver expert-verified labels across 104 anatomical structures. Anno- tation consistency remains a persistent challenge: manual lesion delineation is laborious, operator- dependent, and subject to inter-observer variability, as illustrated by segmentation benchmarks such as LiTS [68]. Regarding clinical representativeness, CT datasets range from broad population-based cohorts like NLST [69] to small, specific single-center collections (e.g. 3D-IRCADb), whereas multi-institution benchmarks like AMOS [70] (500 CT + 100 MRI scans, collected across multiple centers and vendors) better reflect real-world diversity in scanner types and imaging protocols [70]. CT Datasets by anatomical structures. CT datasets show strong concentration in lung/chest ap- plications, driven by large-scale screening programs and COVID-19 research. Whole-body datasets represent an emerging trend for foundation model development, while traditional abdominal and bone imaging remain important clinical applications. 1) Lung/Chest (96 datasets, 279,285 volumes). This dominant category reflects CT’s primary clinical role in thoracic imaging. Major applications include COVID-19 analysis (STOIC2021 (#519) with 10,735 volumes, COV19-CT-DB (#521) with 7,750 volumes), lung cancer screening (NLST (#641) with 26,254 volumes), chest abnormalities detection (CT-RATE (#480) with 50,188 volumes), and nodule detection (LUNA16 (#507) with 888 volumes, LIDC-IDRI (#638) with 1,018 volumes). The category benefits from extensive public health initiatives and automated screening demands. 30 2) Whole-body (7 datasets, 123,557 volumes). An emerging category driven by foundation model development needs. Key datasets include M3D (#481) (120,000 volumes), TotalSegmentator (#472) (1,204 volumes), and AutoPET series (#473) (2,233 volumes combined). These comprehensive collections enable multi-organ segmentation and cross-anatomical learning. 3) Abdomen (55 datasets, 46,305 volumes). Traditional CT application focusing on multi-organ segmentation and tumor analysis. Notable collections include AbdomenAtlas (#479) (20,460 vol- umes), FLARE series (#484) (7,311 volumes combined), AbdomenCT-1K (#498) (1,062 volumes), and specialized organ datasets like KiTS series (#494) for kidney analysis (1,329 volumes com- bined). These datasets support both organ-specific and comprehensive abdominal analysis. 4) Bone/Spine (10 datasets, 41,641 volumes). Specialized orthopedic applications including CT- Pelvic1K (#548) (1,184 volumes), CTSpine1K (#547) (1,005 volumes), VerSe series (#545) (460 volumes combined), and RibFrac2020 (#549) (660 volumes). Tasks focus on bone segmentation, fracture detection, and spinal analysis. 5) Head and Neck (21 datasets, 8,969 volumes). Applications in radiation therapy planning and head/neck cancer treatment. Key datasets include HECKTOR series (#542) (1,462 volumes com- bined), SegRap2023 (#529) (400 volumes), and various structural segmentation challenges. 6) Brain (19 datasets, 4,887 volumes). CT brain imaging focuses on emergency applications includ- ing stroke detection (ISLES 2024 (#527) with 250 volumes), hemorrhage analysis (InSTANCE2022 (#526) with 200 volumes), and trauma assessment. Most brain imaging utilizes MRI, with CT serv- ing specialized acute care roles. CT Datasets by Tasks. CT datasets demonstrate strong task diversity, with segmentation dom- inating due to CT’s excellent structural contrast. Classification applications leverage large-scale screening datasets, while specialized tasks like reconstruction and registration support advanced imaging workflows. 1) Segmentation (150 datasets, 266,862 volumes). Segmentation represents the dominant task cate- gory, reflecting CT’s strength in structural imaging. Applications include multi-organ segmentation (TotalSegmentator (#472) with 1,204 volumes, AbdomenAtlas (#479) with 20,460 volumes, M3D (#481) with 120,000 volumes), organ-specific segmentation (KiTS series (#494) with 1,329 vol- umes for kidneys, LiTS (#490) with 201 volumes for liver), and specialized targets like airway segmentation (AIIB23 (#509) with 312 volumes) and fracture detection (RibFrac2020 (#549) with 660 volumes). 2) Classification (93 datasets, 206,483 volumes). Classification tasks focus on disease screening and diagnostic applications. Major datasets include chest abnormalities detection (CT-RATE (#480) with 50,188 volumes), COVID-19 severity assessment (STOIC2021 (#519) with 10,735 volumes), lung cancer screening (NLST (#641) with 26,254 volumes), and various cancer staging applications across TCGA collections. These datasets enable automated diagnosis and population-level screen- ing. 3) Reconstruction (5 datasets, 130,668 volumes). Emerging task category driven by dose reduction and image enhancement needs. Key datasets include M3D (#481) (120,000 volumes) for multi- modal reconstruction, LDCT-and-Projection-data (#637) (299 volumes) for low-dose reconstruction, and specialized synthesis applications. 4) Localization (6 datasets, 124,107 volumes). Localization tasks primarily support workflow au- tomation and anatomical reference. The M3D dataset (#481) (120,000 volumes) provides compre- hensive localization annotations across multiple organs and structures. 5) Registration (20 datasets, 123,382 volumes). Registration applications focus on longitudinal analysis and multi-modal fusion. Key datasets include Learn2Reg series (#535) for lung CT (450 volumes) and abdomen CT-CT/MR-CT registration (#537) (172 volumes), supporting motion cor- rection and atlas construction. 6) Detection (20 datasets, 52,542 volumes). Detection tasks target specific anatomical structures and pathological findings. Notable applications include pulmonary nodule detection (LUNA16 (#507) with 888 volumes, LIDC-IDRI (#638) with 1,018 volumes), pulmonary embolism detection (RSNA STR (#657) with 12,195 volumes), and lesion detection across various organs. 31 4.3 MRI Volumes Magnetic Resonance Imaging (MRI) provides rich soft-tissue contrast and diverse sequence types for volumetric analysis. We identified 231 3D MRI datasets comprising approximately 523,847 vol- umes in total, as summarized in Table 22. These datasets span a wide range of sequences (T1, T2, FLAIR, DWI) and specialized protocols, varying greatly in scale and focus, from small studies such as MRBrainS13 (#899) for brain tissue segmentation[71] to large-scale resources like OpenMind (#963)[72]. Dataset quality is shaped by sequence heterogeneity and scanner variability: standard- ized collections such as BraTS [73] enforce uniform preprocessing across four canonical sequences, whereas multi-site datasets like OASIS-3 (#756)[74] include heterogeneous acquisition protocols and magnetic field strengths. Annotation consistency remains challenging; peritumoral or edema- related boundaries are known to be more ambiguous than enhancing or core regions in brain tu- mor tasks, contributing to inter-observer variability. In terms of clinical representativeness, MRI datasets range from healthy young adult cohorts in HCP (#839)[75] to elderly dementia popula- tions in ADNI (#816)[76], while multi-vendor datasets such as M&Ms (#735) (Siemens, Philips, GE, Canon) capture broader scanner and protocol diversity and highlight persistent cross-vendor generalization gaps. MRI Datasets by anatomical structures. MRI is predominantly used in neuroimaging, with brain datasets dominating the 3D MRI landscape. Cardiac and abdominal applications show more limited representation, though they provide valuable specialized resources. 1) Brain/Neuro (155 datasets, 356,751 volumes). The brain represents the most studied anatomy in 3D MRI, featuring major collections including BraTS series for tumor segmentation (BraTS 2023 (#802) with 5,880 volumes, UPENN-GBM (#942) with 3,680 volumes), Alzheimer’s research datasets (OASIS-3 (#756) with 5,699 volumes, ADNI (#816) with 2,500 volumes, TADPOLE (#779) with 1,667 volumes), stroke studies (ISLES 2022 (#788) with 400 volumes), and multiple sclerosis research (MSSEG-2 (#780) with 100 volumes). Brain datasets dominate the 3D MRI landscape in both dataset count and total volumes. 2) Head and Neck (3 datasets, 114,643 volumes). Dominated by the OpenMind collection (#963) (114,570 volumes), which represents a breakthrough in large-scale MR data collection. Other datasets include specialized head and neck cancer applications (AAPM-RT-MAC (#897) with 55 volumes). 3) Prostate (15 datasets, 3,704 volumes). Prostate MRI represents a well-established clinical appli- cation, with notable collections including PI-CAI (#776) (1,500 volumes), Prostate-MR-US-Biopsy (#772) (1,151 volumes for fusion imaging), PROSTATEx (#764) (204 volumes for classification), Prostate-MR-Segmentation (#773) (116 volumes), and PROMISE12 (#761) (50 volumes for seg- mentation). These datasets support cancer diagnosis, treatment planning, and MR-US fusion work- flows. 4) Breast (11 datasets, 3,262 volumes). Breast MRI applications include Duke-Breast-Cancer- MR (#834) (922 volumes), I-SPY1 (#813) (847 volumes), I-SPY2 (#944) (719 volumes), ACRIN- Contralateral-Breast-MR (#814) (984 volumes), and specialized collections. These datasets support cancer diagnosis, treatment response assessment, and radiomics research. 5) Cardiac (13 datasets, 2,991 volumes). Cardiac MRI datasets focus on ventricular/myocardial segmentation and functional quantification. Key collections include M&Ms (#735) (375 volumes), M&Ms-2 (#736) (360 volumes), LAScarQS++ 2024 (#738) (200+ volumes), MyoPS++ 2024 (#740) (200+ volumes), ACDC (#734) (150 volumes), and EMIDEC (#881) (150 volumes). These datasets support automated cardiac analysis and multi-center validation studies. 6) Knee (2 datasets, 1,823 volumes). Include MRNet (#880) (1,370 volumes for knee abnormali- ties detection) and SKI10 (#778) (150 volumes for cartilage segmentation), supporting orthopedic applications and sports medicine research. 7) Others (17 datasets, 1,539 volumes). Include liver applications (LLD-MMR2023 (#935) with 498 volumes), spine imaging, gastrointestinal tract studies, and various specialized anatomical regions. 8) Whole-body (2 datasets, 1,016 volumes). Include TotalSegmentator MRI (#733) (616 volumes) and UW-Madison GI Tract (#883) (467 volumes), providing comprehensive anatomical coverage for foundation model development. 32 MRI Datasets by Tasks. 3D MRI datasets are predominantly designed for segmentation and clas- sification tasks, reflecting MRI’s strength in soft-tissue contrast and anatomical delineation. The task distribution aligns with MRI’s clinical applications in detailed tissue analysis and disease char- acterization. 1) Classification (80 datasets, 322,508 volumes). Classification represents the largest category by to- tal volumes, dominated by the OpenMind collection (#963) (114,570 volumes) and large-scale neu- roimaging studies. Major applications include Alzheimer’s disease classification (OASIS-3 (#756) with 5,699 volumes, ADNI (#816) with 2,500 volumes, TADPOLE (#779) with 1,667 volumes), population studies (Human Connectome Project (#839) with 1,206 volumes, Brain Genomics Su- perstruct Project (#838) with 1,570 volumes), and cancer staging (PROSTATEx (#764) with 204 volumes for prostate cancer). These datasets enable automated diagnosis, disease staging, and population-level brain research. 2) Segmentation (114 datasets, 151,433 volumes). Segmentation represents the largest category by dataset count, leveraging MRI’s excellent soft-tissue contrast. Major applications include brain tumor delineation (BraTS 2023 (#802) with 5,880 volumes, UPENN-GBM (#942) with 3,680 vol- umes, MSD01_BrainTumor (#803) with 750 volumes), cardiac segmentation (M&Ms (#735) with 375 volumes, ACDC (#734) with 150 volumes), prostate segmentation (PI-CAI (#776) with 1,500 volumes, Prostate-MR-US-Biopsy (#772) with 1,151 volumes), and whole-body segmentation (To- talSegmentator MRI (#733) with 616 volumes). The diversity in anatomical targets reflects MRI’s versatility in tissue delineation. 3) Reconstruction (15 datasets, 127,464 volumes). MR reconstruction focuses on acceleration tech- niques and image enhancement. Key datasets include fastMR (#809) (1,594 volumes), CMRxRecon (#930) (300 volumes for cardiac reconstruction), and OpenMind (#963) which also supports re- construction tasks. This category addresses critical clinical needs for faster MR acquisition and improved image quality. 4) Registration (31 datasets, 17,808 volumes). Registration applications include multi-timepoint studies, atlas construction, and multi-modal fusion. Notable datasets include Learn2Reg series (#757) (OASIS, Hippocampus, LUMIR), CuRIOUS series (#743) for MR-US registration, and vari- ous longitudinal studies for disease progression monitoring. These datasets enable temporal analysis and cross-modal alignment. 5) Tracking (5 datasets, 1,855 volumes). Motion tracking applications primarily in cardiac MRI, including STACOM 2011 (#907) (1,158 volumes) for cardiac motion analysis and various diffusion tractography studies. These datasets support dynamic analysis and fiber tracking applications. 6) Detection (4 datasets, 1,245 volumes). Detection tasks focus on automated identification of anatomical landmarks and pathological structures, including aneurysm detection (ADAM2020 (#956) with 255 volumes) and various brain pathology identification tasks. 4.4 Ultrasound Volumes 3D ultrasound provides real-time volumetric imaging widely used for interventional guidance and multi-modal fusion. We identify 27 ultrasound-related 3D datasets containing approximately 56,609 volumes, as summarized in Table 23. Most of these datasets appear within multi-modal collections (e.g., US/MR or US/CT), reflecting ultrasound’s predominant role in image-guided and fusion-based clinical workflows rather than as a standalone modality. Data quality is strongly operator-dependent, with clinical acquisitions showing higher variability compared to controlled research settings (e.g., the CuRIOUS series (#979)[77]). In terms of representativeness, existing 3D ultrasound datasets are primarily derived from high-end interventional systems, underrepresenting handheld or point- of-care imaging scenarios common in real-world clinical practice. Ultrasound Datasets by anatomical structures. The available 3D ultrasound datasets span di- verse anatomical regions, with multi-modal combinations being particularly common for registra- tion and fusion applications. 1) Brain (9 datasets,∼500 volumes). Brain ultrasound datasets focus primarily on US-MR registra- tion for neurosurgical guidance. The CuRIOUS series (#979) (2018, 2019, 2022) provides datasets for brain tumor applications, while Learn2Reg LUMIR (#987) (269 volumes) supports multi-modal 33 registration research. These datasets enable US-guided brain interventions and intraoperative navi- gation. 2) Cardiac (3 datasets, ∼1,400 volumes). Cardiac ultrasound datasets include STACOM 2011 (#907) (1,158 volumes for motion tracking), CETUS2014 (#965) (45 volumes), and MVSeg- 3DTEE2023 (#966) (175 volumes for mitral valve segmentation). These datasets support automated echocardiography, cardiac function quantification, and structural heart analysis. 3) Prostate (2 datasets,∼1,300 volumes). Prostate datasets focus on US-MR fusion for biopsy guid- ance and treatment planning. Prostate-MR-US-Biopsy (#988) (1,151 volumes) andμ-RegPro2023 (#989) (108 volumes) support fusion imaging applications critical for prostate cancer diagnosis and intervention. 4) Kidney (4 datasets, ∼1,400 volumes). Pediatric kidney datasets from the AREN series (#557) (AREN0532, AREN0533, AREN0534) provide multi-modal collections including ultrasound for Wilms tumor research, supporting both classification and segmentation tasks in pediatric oncology. 5) Breast (1 dataset, 200 volumes). TDSC-ABUS2023 (#964) provides automated breast ultrasound data for breast cancer detection, supporting segmentation, classification, and detection tasks in breast imaging screening workflows. 6) Other Abdominal Organs (8 datasets,∼1,100 volumes). Include pancreas (CPTAC-PDA (#598)), liver (AHEP0731 (#625)), uterus (CPTAC-UCEC (#600)), and other organs from multi-modal can- cer imaging collections, primarily supporting classification tasks for oncological applications. Ultrasound Datasets by Tasks. 3D ultrasound datasets are dominated by registration applications, reflecting the modality’s role in multi-modal image fusion and guidance systems. 1) Registration (15 datasets,∼2,000 volumes). Registration represents the dominant task category, reflecting ultrasound’s critical role in real-time guidance and multi-modal fusion. Major applications include US-MR brain registration (CuRIOUS series (#979)), prostate fusion imaging (Prostate-MR- US-Biopsy (#988),μ-RegPro2023 (#989)), cardiac motion tracking (STACOM 2011 (#907)), and multi-modal brain registration (Learn2Reg LUMIR (#987)). This dominance reflects ultrasound’s primary clinical value in providing real-time guidance for interventions and fusion with other imag- ing modalities. 2) Classification (10 datasets,∼1,800 volumes). Classification tasks focus primarily on cancer stag- ing and diagnosis across multiple organs, including kidney tumors (AREN series (#557)), pancreatic cancer (CPTAC-PDA (#598)), liver cancer (AHEP0731 (#625)), and other malignancies. These ap- plications leverage ultrasound’s accessibility for screening and staging workflows. 3) Segmentation (8 datasets, ∼800 volumes). Segmentation applications target organ and struc- ture delineation for cardiac analysis (CETUS2014 (#965), MVSeg-3DTEE2023 (#966)), tumor seg- mentation (AREN0533-Tumor-Annotations (#559), AREN0534 (#561)), and breast lesion detection (TDSC-ABUS2023 (#964)). These datasets support automated measurement and volumetric analy- sis critical for clinical assessment. 4) Detection (1 dataset, 200 volumes). Detection tasks focus on automated lesion identification, exemplified by TDSC-ABUS2023 (#964) for breast cancer screening, supporting computer-aided detection workflows in clinical practice. 4.5 PET Volumes Positron Emission Tomography (PET) provides functional information complementary to anatom- ical imaging. Public PET volumes are scarce and often appear in multi-modality collections (e.g., PET/CT, PET/MR). We identify 65 PET-related 3D datasets with 95,456 volumes in total, as pre- sented in Table 24. These collections span diverse anatomic regions with a strong focus on oncol- ogy applications, particularly in lung/chest (15 datasets), head and neck (11 datasets), and brain (8 datasets) regions. This significant expansion largely comes from comprehensive cancer imaging archives, multi-center studies, and large-scale neuroimaging initiatives. PET Datasets by anatomical structures. These datasets use PET primarily for oncology applica- tions across various anatomical regions, though multi-modal combinations are the norm rather than 34 the exception. The distribution shows clear preferences for certain anatomical regions where PET imaging provides the most clinical value. 1) Lung/Chest (15 datasets, ∼55,000+ volumes). This represents the largest category by dataset count, reflecting PET’s critical role in lung cancer diagnosis and staging. Key collections include QIDW (#646) (52,000 volumes for quality assurance), Lung-PET-CT-Dx (#639) (355 volumes), CPTAC-LUAD (#597) (244 volumes), ACRIN-NSCLC-FDG-PET (#1001) (242 volumes), CPTAC- LSCC (#596) (212 volumes), and NSCLC-Radiogenomics (#603) (211 volumes). The dominance of lung-related datasets demonstrates PET’s established clinical utility in pulmonary oncology. 2) Head and Neck (11 datasets, ∼4,200 volumes). Head and neck cancers represent a major ap- plication area for PET imaging, with notable collections including HECKTOR 2022 (#544) (883 volumes), HNSCC (#565) (627 volumes), TCGA-HNSC (#576) (479 volumes), HECKTOR 2021 (#543) (325 volumes), Head-Neck-PET-CT (#634) (298 volumes), QIN-HEADNECK (#615) (279 volumes), and ACRIN-HNSCC-FDG-PET-CT (#998) (260 volumes). These datasets support both tumor segmentation and treatment response assessment. 3) Brain (8 datasets,∼13,300 volumes). Brain PET datasets focus primarily on neurodegenerative diseases and provide the largest individual dataset volumes. Major collections include OASIS-3 (#756) (5,699 volumes), ADNI (#816) (2,500 volumes), TADPOLE (#779) (1,667 volumes), and PPMI (#1037) (683 volumes) for Alzheimer’s and Parkinson’s disease research, alongside smaller oncology-focused datasets like ACRIN-FMISO-Brain (#1000) (45 volumes). 4) Abdominal Organs (7 datasets,∼1,400 volumes). Include specialized datasets for liver, pancreas, and kidney imaging. Notable collections include AREN0532 (#557) (544 volumes) and AREN0534 (#561) (239 volumes) for pediatric kidney tumors, AHEP0731 (#625) (190 volumes) for liver cancer, and CPTAC-PDA (#598) (168 volumes) for pancreatic cancer. 5) Whole-body/Multi-organ (3 datasets, ∼2,300 volumes).Comprehensive whole-body PET datasets include AutoPET I (#993) (1,219 volumes), AutoPET (#992) (1,014 volumes), and fastPET-LD (#1042) (68 volumes), providing valuable resources for pan-cancer detection and seg- mentation tasks. 6) Breast (3 datasets, ∼240 volumes).Specialized breast cancer datasets include BREAST- DIAGNOSIS (#628) (88 volumes), ACRIN-FLT-Breast (#999) (83 volumes), and QIN-Breast (#614) (68 volumes), supporting breast cancer diagnosis and treatment monitoring. PET Datasets by Tasks. PET datasets reflect the modality’s primary clinical applications in on- cology and neurology, with task distribution strongly aligned with PET’s role in functional and metabolic imaging for disease diagnosis, staging, and treatment monitoring. 1) Classification (45 datasets,∼60,000+ volumes). Classification represents the dominant task cat- egory, reflecting PET’s core clinical utility in disease staging, treatment response assessment, and diagnostic classification. Oncology applications span multiple cancer types, including lung cancer datasets (CPTAC-LUAD (#597) with 244 volumes, ACRIN-NSCLC-FDG-PET (#1001) with 242 volumes, TCGA-LUSC (#1012) with 37 volumes), head and neck cancer studies (TCGA-HNSC (#576) with 479 volumes, ACRIN-HNSCC-FDG-PET-CT (#998) with 260 volumes), and various other malignancies across different anatomical sites. Neurological applications focus on neurode- generative diseases, particularly Alzheimer’s disease classification (OASIS-3 (#756) with 5,699 vol- umes, ADNI (#816) with 2,500 volumes, TADPOLE (#779) with 1,667 volumes) and Parkinson’s disease research (PPMI (#1037) with 683 volumes). The dominance of classification tasks aligns with PET’s clinical role in providing metabolic information for staging and prognosis. 2) Segmentation (20 datasets, ∼25,000 volumes). Segmentation tasks focus primarily on tumor delineation and organ-at-risk identification for radiation therapy planning. Major collections include AutoPET I (#993) (1,219 volumes), AutoPET (#992) (1,014 volumes), HECKTOR 2022 (#544) (883 volumes), and HNSCC (#565) (627 volumes). These datasets support automated tumor volume definition, which is critical for radiotherapy planning and treatment monitoring. The emphasis on head and neck, lung, and whole-body segmentation reflects PET’s established role in oncology workflow integration. 3) Multi-task datasets (8 datasets,∼5,000 volumes). Several datasets provide annotations for mul- tiple tasks, enabling comprehensive analysis approaches. Examples include Head-Neck-PET-CT 35 (#634) (298 volumes for both segmentation and classification), NSCLC-Radiogenomics (#603) (211 volumes for segmentation and classification), and ACRIN-FMISO-Brain (#1000) (45 volumes for segmentation and classification). This multi-task approach reflects the clinical reality where PET images are used for multiple diagnostic and therapeutic purposes simultaneously. 4) Detection (3 datasets, ∼400 volumes). Detection tasks focus on lesion identification and local- ization, exemplified by Lung-PET-CT-Dx (#639) (355 volumes for classification and detection) and fastPET-LD (#1042) (68 volumes for detection). While less common than classification, detection tasks are important for automated screening and lesion characterization in clinical workflows. 5) Registration (3 datasets, ∼1,200 volumes). Registration applications appear primarily in the HECKTOR series (#543) (2021 and 2022), supporting multi-timepoint analysis for treatment re- sponse assessment. This reflects PET’s growing role in longitudinal monitoring of therapy effects and disease progression. 4.6 Other 3D Volumes Beyond the major modalities, we collect 26 3D datasets from specialized imaging techniques with 5,381+ volumes in total, as presented in Table 25. These modalities serve specific clinical niches and emerging applications, with OCT dominating the collection due to large-scale ophthalmology datasets. The diversity reflects the evolution of medical imaging technology and specialized clinical needs. Other Modalities According to Imaging Technology. Each modality addresses specific clini- cal applications and anatomical targets, with OCT leading in volume due to comprehensive retinal imaging datasets. 1) Optical Coherence Tomography (OCT) (14 datasets, 4,288+ volumes). OCT dominates this cat- egory, primarily targeting retinal and ophthalmologic applications. The OLIVES dataset (#1071) alone contributes 1,268 volumes for diabetic condition analysis, while the newly added OCTA-500 dataset (#1080) provides 500 volumes for comprehensive retinal OCTA analysis. Specialized col- lections include GAMMA (#1072) (300 volumes for glaucoma analysis), RETOUCH (#1068) (112 volumes for retinal disease segmentation), and various Duke University datasets for age-related macular degeneration and diabetic macular edema. The OCTA2024 dataset (#1081) supports ad- vanced OCT to OCTA translation research. Tasks primarily focus on classification, segmentation, and reconstruction of retinal pathologies, supporting automated screening for eye diseases. 2) Digital Subtraction Angiography (3D DSA) (4 datasets, 454 volumes). DSA applications fo- cus on cerebrovascular imaging, particularly aneurysm detection and analysis. Key datasets in- clude CADA series (#1059) for cerebral aneurysm detection (372 volumes combined) and SHINY- ICARUS (#1060) for internal carotid artery aneurysm segmentation (82 volumes). These datasets support critical neurovascular intervention planning and risk assessment. 3) Cone-beam CT (CBCT) (4 datasets, 581 volumes). CBCT serves specialized applications in dental imaging and treatment planning. Notable collections include ToothFairy2023 (#1067) for dental surgery planning (443 volumes), pancreatic CT-CBCT registration datasets (40 volumes), and pelvic reference data for prostate cancer treatment (58 volumes). These datasets bridge diagnostic and interventional imaging workflows. 4) 3D Microscopy (3 datasets, 54 volumes). Microscopy datasets target cellular and subcellular anal- ysis, including MitoEM (#1056) for mitochondrial ultrastructure (2 volumes), platelet ultrastructure analysis (2 volumes), and prostate cancer pathology (50 volumes). Though small in volume, these datasets enable high-resolution structural analysis at the cellular level. Other Modalities According to Tasks. Task distribution reflects the specialized nature of these modalities, with classification dominating due to large-scale OCT screening applications. 1) Classification (11 datasets, 3,914 volumes). Classification tasks predominantly target disease screening and diagnosis, especially in ophthalmology. Major applications include diabetic condition screening (OLIVES (#1071) with 1,268 volumes), glaucoma detection (OCT Glaucoma Detection (#1070) with 1,110 volumes), and various retinal disease classification tasks. These datasets enable automated screening systems for population health initiatives. 36 2) Segmentation (17 datasets, 1,987 volumes). Segmentation applications span multiple modalities and anatomical targets, from retinal layer segmentation in OCT to aneurysm delineation in DSA and dental structure segmentation in CBCT. The diversity of targets reflects the specialized nature of each modality’s clinical applications. 3) Registration (3 datasets, 498 volumes). Registration tasks primarily support treatment planning and longitudinal analysis, including CBCT-CT registration for radiation therapy and structural- functional alignment in ophthalmology. 4) Reconstruction/Translation (1 dataset, TBD volumes). Advanced reconstruction and translation tasks include OCT to OCTA image translation, enabling cross-modal analysis and synthetic data generation for retinal imaging applications. These specialized modalities complement major imaging modalities by addressing specific clini- cal needs and emerging applications, contributing to the comprehensive landscape of 3D medical imaging datasets. 4.7 Challenges and Opportunities The 3D medical imaging landscape presents unique challenges and opportunities that distinguish it from 2D medical imaging. Despite providing richer spatial information essential for volumetric analysis and clinical decision-making, 3D datasets remain significantly constrained by fundamental limitations in data acquisition, annotation complexity, and resource allocation. Key Challenges in 3D Medical Imaging Datasets. The primary challenges stem from the inher- ent complexity and cost of 3D data acquisition and processing. High acquisition and annotation costs represent the most significant barrier, as 3D imaging requires expensive specialized equipment (CT, MRI, and PET scanners) and expert radiologists for volumetric annotation, resulting in the modest growth observed compared to 2D datasets. This economic constraint directly impacts data availability and diversity. Dataset overlap and duplication presents another critical challenge that researchers must be aware of when conducting external validation studies. Some datasets in our collection contain overlapping or identical data under different names, particularly when larger datasets consolidate multiple smaller collections. For instance, the OCT2017 dataset and MedMNIST OCT dataset contain identical reti- nal OCT images, as MedMNIST integrates multiple publicly available datasets including OCT2017. Similar overlaps exist across other modalities where comprehensive datasets merge smaller special- ized collections. Researchers should exercise caution when selecting datasets for external validation to avoid inadvertently using overlapping data that could lead to overly optimistic performance esti- mates and compromised generalizability assessments. Complexity and cost. On the data side, challenges are multifaceted. Acquisition costs remain pro- hibitively high due to the expense of imaging hardware, long scanning times, and patient compliance issues. Storage costs escalate rapidly as each volumetric scan can range from hundreds of megabytes to several gigabytes, requiring robust archiving infrastructure. Annotation costs are substantial be- cause volumetric segmentation demands time-consuming, slice-by-slice delineation by expert radi- ologists. On the model side, these data characteristics translate into significant computational chal- lenges. The high dimensionality of 3D medical images substantially increases memory consumption and processing time during training and inference, often necessitating specialized hardware and op- timization strategies. Moreover, the low signal-to-noise ratio of many volumetric acquisitions and the small size of pathological regions further complicate feature extraction and model generaliza- tion. Together, these factors underscore the intricate interplay between data and model complexity in 3D medical imaging research. Modality and anatomical imbalances create substantial gaps in representation. While CT (261 datasets, 753,421 volumes) and MRI (231 datasets, 523,847 volumes) dominate the landscape, crit- ical modalities like ultrasound (27 datasets, 56,609 volumes), PET (65 datasets, 95,456 volumes), and emerging volumetric techniques remain underrepresented relative to their clinical importance. Anatomically, while the concentration on brain and abdomen/liver regions has expanded signifi- cantly, cardiac, musculoskeletal, and certain specialized applications still have relatively limited resources, though recent large-scale initiatives are beginning to address these gaps. 37 Task-specific limitations further constrain the utility of existing 3D datasets. The overwhelming dominance of classification and segmentation tasks, while clinically important, reflects the field’s incomplete transition from task-oriented to foundation-oriented data engineering paradigms. Reg- istration and reconstruction tasks remain underrepresented despite their critical importance for lon- gitudinal studies and treatment monitoring. Additionally, the scarcity of multi-task datasets limits the development of versatile clinical AI systems capable of handling complex, real-world diagnostic workflows. Opportunities for Advancement. Despite these challenges, the 3D medical imaging domain presents remarkable opportunities for transformative advancement. The substantial collection of unlabeled 3D volumes (219 datasets with hundreds of thousands of volumes) offers unprecedented potential for self-supervised learning and contrastive pretraining. Large repositories like TCIA for CT and HCP for MRI provide the scale necessary for foundation model pretraining, while multi- sequence MR data enables sophisticated cross-modal consistency training and modality dropout techniques. Foundation model-driven data augmentation emerges as a particularly promising direction. Well- trained generative foundation models can participate in semi-supervised learning frameworks, gen- erating synthetic 3D volumes that reflect real clinical presentations while addressing privacy con- straints. This approach is especially valuable for rare diseases and underrepresented anatomical regions where data acquisition remains challenging. Multimodal integration presents opportunities to leverage complementary information across imag- ing modalities. PET/CT and PET/MR combinations demonstrate the clinical value of multimodal approaches, while the emergence of vision-language datasets that combine 3D volumes with clini- cal reports and radiology texts opens new possibilities for cross-modal reasoning and clinical con- text understanding. Beyond traditional imaging modality combinations, innovative cross-domain multimodal approaches are emerging, such as integrating macroscopic imaging (CT/MRI) with mi- croscopic pathology data. These pathology-imaging combinations offer unique opportunities to bridge the gap between radiological findings and histological ground truth, enabling AI systems to learn from both macroscopic anatomical structures and microscopic tissue characteristics. Such approaches can significantly enhance diagnostic accuracy by combining CT’s ability to detect and localize lesions with pathology’s role as the diagnostic gold standard, creating more robust and clinically-relevant AI systems. Advances in cross-modal alignment techniques enable more sophis- ticated fusion strategies that can favorably enhance diagnostic capabilities across these diverse data types. Multi-task learning paradigms offer transformative potential for 3D medical imaging, analogous to the "one-for-all" paradigm exemplified by ChatGPT in natural language processing. Rather than training separate models for individual tasks, integrated frameworks can simultaneously address multiple tasks (e.g., segmentation, classification, and detection) within unified architectures. This approach not only improves computational efficiency but also enables knowledge transfer across related tasks, particularly valuable given the limited scale of individual 3D datasets. Multi-task datasets that provide diverse annotation types for the same volumetric data can unlock synergistic learning effects, where performance on individual tasks benefits from joint optimization across mul- tiple objectives. The vision of a unified diagnostic and generative model that can handle multiple clinical tasks simultaneously represents a paradigm shift toward more versatile and efficient clinical AI systems, similar to how foundation models have revolutionized natural language understanding and generation. Looking forward, the transition toward foundation-oriented data engineering paradigms demands fundamental changes in how 3D medical datasets are conceptualized and structured. Future dataset designs should prioritize adaptability and extensibility, enabling researchers to derive new tasks and applications from existing resources. Strategic dataset consolidation through systematic metadata harmonization, combined with advances in self-supervised learning and cross-modal reasoning, po- sitions the 3D medical imaging domain for significant breakthroughs in clinical AI applications. 38 (a) (b) (c) Figure 15: The distribution of different (a) modalities, (b) anatomical structures, and (c) tasks for video datasets 5 Medical Video Datasets Medical video datasets are crucial resources for developing algorithms that leverage spatiotemporal information in dynamic clinical scenarios, such as minimally invasive surgery, medical education, and video-based diagnosis. In contrast to static image datasets, video data facilitates the model- ing of motion patterns, procedural workflows, and temporal consistencies, which are essential for tasks such as surgical instrument tracking, cross-frame anatomical structure segmentation, or phys- iological motion estimation. This survey identifies 77 medical video datasets, comprising a total of 166,691 samples. These datasets span a diverse range of tasks, imaging modalities, and anatomical structures. All video datasets are illustrated in the Tab. 26. 5.1 Overview Fig. 15 illustrates the distribution of video datasets across different anatomical structures, imaging modalities, and tasks. The most prevalent anatomical structures are the stomach, colon, and esopha- gus, with each category individually accounting for about 30% of the total videos. In contrast, other anatomical structures, such as the retina, heart, pupil, and iris, are significantly underrepresented, each constituting less than 2% of the total collection. Similarly, the distribution across imaging modalities is highly skewed, with endoscopy alone accounting for a substantial 85.9% of the videos. Consequently, modalities such as ultrasound microscopy, and RGB remain scarce, which highlights a critical need for larger-scale datasets to mitigate potential modality bias, particularly in the devel- opment of foundation models. In contrast to the severe imbalances observed across modalities, the task distribution is more moderate, though still demonstrably long-tailed. Classification, detection, and segmentation represent the most common tasks, followed by estimation, generation, and VQA, whereas tracking, retrieval, and registration are notably underrepresented, warranting further investi- gation. Given the severe modality imbalance, task-level information helps distinguish the properties of the collected video datasets. Therefore, the organizational structure of this section deviates from that of Sections 3 and 4. Specifically, the video datasets are introduced primarily based on their as- sociated tasks rather than modality, with a supplementary analysis of the corresponding modalities and anatomical structures. 5.2 Task Below we introduce the major tasks in the collected video datasets. Figure 16 demonstrates common tasks in medical video datasets, including video classification, video segmentation, video detection, video tracking, video estimation, and video registration. 5.2.1 Classification Classification in medical videos involves assigning categorical labels to entire sequences or specific temporal segments by leveraging spatial-temporal features. This task is fundamental to a wide range of clinical and surgical applications, such as surgical phase recognition, skill assessment, and disease diagnosis. To date, 40 datasets comprising 81,701 samples have been identified for this purpose, with endoscopy dominating as the primary modality. Performance is commonly evaluated using metrics 39 such as accuracy, F1-score, and the Area Under the ROC Curve (AUC), while temporal metrics like the segmental edit score are also employed to assess sequence-level consistency. Representative datasets span diverse surgical domains. Cholec80 (#1098) and its derivatives, in- cluding CholecT50 (#1082) and the CholecTriplet challenges (#1083),(#1107), provide laparoscopic cholecystectomy videos annotated with surgical phases, instrument presence, and fine-grained <in- strument–verb–target> triplets. These resources serve as benchmarks for workflow analysis and activity recognition. SurgVisDom (#1084) contains 488 bowel surgery videos with phase anno- tations, enabling cross-domain generalization studies. As the largest public dataset for gastroin- testinal endoscopy, HyperKvasir (#1152) is the largest publicly available gastrointestinal endoscopy dataset, comprising 373 videos and over 110,000 video frames annotated for anatomy and pathol- ogy, supporting classification, localization, and captioning. In the field of ophthalmic surgery, the CATARACTS (#1085) and Cataract-1K datasets (#1130) provide microscopy videos annotated for surgical phases, instruments, and pixel-level segmentation, facilitating multi-task modeling. Other influential resources include the EndoVis Workflow and Skill Assessment (SWSA) series (#1087) for phase and skill classification, and SAR-RARP50 (#1127), the first public robot-assisted radical prostatectomy dataset with synchronized action and instrument annotations. 5.2.2 Segmentation Medical video segmentation involves the frame-by-frame delineation of anatomical structures, pathological regions, or surgical instruments to enable precise spatio-temporal analysis. This task is crucial for applications including real-time surgical guidance, quantitative organ motion track- ing, and automated assessment of lesion dynamics. Our review encompasses 32 datasets tailored for segmentation, containing a total of 18,739 video instances. Performance is typically evaluated using metrics such as the Dice similarity coefficient, Intersection-over-Union (IoU), and pixel-level accuracy. Representative datasets highlight both surgical and microscopic domains. The Robotic Instru- ment Segmentation (RIS) (#1116) and Kidney Boundary Detection (KBD) (#1114) datasets in- troduced pixel-level annotations for robotic surgical tools and anatomical boundaries, establish- ing early benchmarks for intraoperative vision. In ophthalmology, Cataract-1K (#1130) combines phase annotations with 2,256 manually segmented frames for cataract surgery, enabling joint analy- sis of workflow and fine-grained structures. The HyperKvasir dataset (#1152), while primarily used for gastrointestinal classification, also includes segmentation masks for anatomical landmarks and pathological findings across 373 endoscopic videos. More recent challenges extend segmentation to complex multi-modal and 3D contexts, For instance, P2ILF (#1120) combines laparoscopic video and CT for liver landmark delineation, while SAR-RARP50 (#1127) is the first public dataset of robot-assisted radical prostatectomy videos with synchronized instrument segmentation and action recognition. 5.2.3 Detection Video detection aims to identify and localize target objects, such as lesions, instruments, or anatom- ical landmarks, within individual frames of a video while leveraging temporal continuity to improve robustness. This capability is crucial for early disease screening, intraoperative navigation, and au- tomated procedural quality assessment. We identified 27 datasets for the detection task. Commonly used evaluation metrics include precision, recall, mean Average Precision (mAP), and frame-level F1-core. In our survey, these 28 datasets comprise 49,507 samples emphasize detection. Evaluations typically report precision, recall, mean Average Precision (mAP) at bounding-box or mask-level IoU thresholds, and frame-level F1. For temporally aggregated predictions, some studies additionally re- port video-mAP or track-aware scores to penalize fragmented detections. Representative resources span lesion, artifact, and instrument detection across multiple surgical do- mains. GIANA (#1110) and EndoCV (#1105) provide endoscopic polyp detection benchmarks with bounding-box or mask annotations, stressing generalization across centers and devices. Instrument- centric datasets include the m2cai16-tool-locations (#1108) set and the large-scale SurgToolLoc challenges (2022–2023) (#1091),(#1125) with tens of thousands of annotated frames for robotic and laparoscopic tools, enabling strong baselines for real-time instrument awareness and down- stream workflow understanding. Beyond the abdomen, ophthalmic datasets such as CATARACTS or Cataract-1K and LensID (#1131) support tool and structure detection in cataract surgery, while 40 PitVis (#1124) focuses on transsphenoidal neurosurgery with step- and instrument-level labels. Broader clinical coverage is offered by AVOS (#1112), a multi-procedure open-surgery corpus with dense annotations that enables cross-procedure detection, tracking, and localization. Recent multi-domain collections such as SARAS-MESAD (#1155) further test robustness by mixing real and phantom data under shared action or instrument vocabularies. Across these datasets, anno- tation granularity ranges from sparsely sampled frames to densely labeled clips, with boxes, in- stance masks, or keypoints. Emerging trends include spatiotemporal tube proposals, joint detection- tracking protocols, and robustness benchmarks under realistic corruptions, which together move detection from frame-wise recognition toward reliable, clinically usable video understanding. 5.2.4 Tracking Tracking in medical videos entails following the spatiotemporal trajectories of objects of interest, such as surgical tools or anatomical landmarks, across consecutive frames. This task is fundamental to applications such as workflow analysis, motion quantification, and dynamic process monitoring. Our survey identified 8 datasets with 2,420 samples dedicated to tracking. The tracking task usually employs metrics such as Multiple Object Tracking Accuracy (MOTA), Multiple Object Tracking Precision (MOTP), identity switches (IDSW), and track purity. Representative datasets focus on the surgical domain. For example, the m2cai16-tool-locations dataset (#1108) provided laparoscopic tool-tip trajectories, while the EndoVis tracking challenges expanded to encompass tracking, tissue motion estimation, and joint detection–tracking tasks. SurgT (#1141) and SARAS-MESAD (#1155) further incorporated stereoscopic views, soft-tissue tracking, and phantom–real domain variations. Beyond endoscopy, STIR (#1142) provided infrared–visible paired videos for surgical tissue tracking, and the large-scale dataset AVOS delivered dense per- frame annotations across 47 hours of open surgery from 23 procedure types. Specialized datasets such as HiSWA-RLLS (#1143) for robotic liver resection and EgoSurgery (#1144) with egocen- tric video plus gaze data highlight emerging subfields, reflecting a recent trend toward multi-task benchmarks that unify detection, segmentation, and temporal association for comprehensive spa- tiotemporal understanding. 5.2.5 Estimation Estimation tasks in medical video analysis aim to derive quantitative variables from temporal se- quences, such as depth maps, motion fields, or physiological parameters. Applications include 3D reconstruction from monocular endoscopic videos, camera pose estimation for navigation, respira- tory motion estimation, and surgical skill scoring. Our survey identified two datasets dedicated to this task. The SimCol-to-3D (#1113) dataset contains simulated colonoscopy videos for depth pre- diction and camera pose estimation, with 15 sequences annotated for both simulated and real pro- cedures, enabling evaluation under controlled and clinical conditions. The challenge also includes a Colposcopy subset with 30 videos for depth estimation in gynecological imaging. The Endovis 2019-SCRE (#1151) dataset contains videos from 9 medical sites for the task of dense depth estima- tion. The corresponding depth maps were obtained from structured light data captured using porcine cadavers. Evaluation metrics are task-specific, including mean absolute error (MAE), endpoint error (EPE), and correlation coefficients. Moreover, recent works have increasingly adopted multi-task formulations that jointly estimate depth, pose, and motion to improve downstream surgical naviga- tion and workflow understanding. 5.2.6 Registration Registration in medical video analysis involves aligning multimodal data, such as 2D video en- doscopy with 3D computed tomography (CT), to establish a consistent spatial correspondence across imaging modalities. This process is crucial for intraoperative guidance, anatomical structure map- ping, and enhanced visualization of surgical fields. In our survey, one datasets with a total of 167 samples were identified for registration tasks. The P2ILF dataset (#1120) provides paired endoscopy videos and CT scans, and is designed for evaluating multimodal registration methods. The dataset included 25 cases (10 for training, 10 for validation, and 5 for testing) with both 3D model and video-endoscopic data, supporting cross- modality alignment and benchmarking registration accuracy. The registration is performed between the landmarks of the 3D model and those extracted from the videos. 41 Lower GI tract Upper GI tract √ × Video ClassificationVideo Segmentation Video Detection Video Estimation Video Tracking Bagging Prostate: 1~4 s Video Registration Figure 16: Demonstration of the collected video datasets from different tasks. The figure for the video estimation task is from the EndoVis 2022-SimCol-to-3D dataset (#1113), and the figure for the video registration task is from the EndoVis 2022-P2ILF dataset (#1120). Endoscopy UltrasoundRGB Microscopy Figure 17: Illustration of four major modalities in the collected video datasets. Evaluation metrics for registration commonly include Target Registration Error (TRE), the Dice Similarity Coefficient (DSC) for segmented structures, and success rates within clinically acceptable error thresholds. Together, the P2ILF dataset form a comprehensive benchmark for developing and validating multimodal registration approaches in minimally invasive liver surgery. 5.3 Modalities Among the 77 medical video datasets identified in our survey, endoscopy constitutes the vast major- ity with 56 datasets, underscoring its central role in documenting dynamic intraoperative and diag- nostic procedures. This prevalence is attributable to several factors: the routine integration of video recording systems in surgical suites, the ease of acquiring high-resolution footage during standard procedures, and the relatively straightforward annotation of visible anatomical structures or surgical tools without requiring complex multi-view reconstruction. Longstanding community initiatives, such as the EndoVis challenges, have further accelerated dataset generation and standardization, fostering a virtuous cycle between benchmark availability and method development. In contrast, other imaging modalities are notably underrepresented. Microscopy videos (10 datasets) are often recorded during ophthalmic surgery to demonstrate detailed anatomical structures of the eye, such as the retina, iris, and pupil. Ultrasound (7 dataset) are infrequently acquired as continuous cine sequences due to clinical workflow constraints and the need for specialized protocols, such as dynamic perfusion studies or echocardiography loops. The RGB refers to videos captured with a camera in open environments and is most often associated with non-surgical scenarios, such as instructional recordings for emergency care, nursing, or simulated surgical procedures. Figure 17 illustrates four major modalities in the collected video datasets. 42 5.4 Anatomical Structures Most of the video datasets (40 datasets) focus on abdominal anatomical structures, with a large pro- portion related to the gallbladder (13 datasets) and the colon (6 datasets). This is because many of these datasets were collected during procedures such as cholecystectomy and endoscopy. Seven datasets focus on eye-related anatomical structures, including the iris and pupil, with most of the videos collected during cataract surgery. The remaining video datasets cover a wide range of anatomical structures across the body, including the thyroid (1 dataset), pituitary (2 dataset) and placenta (1 dataset). A portion of the videos were also collected from non-human structures, such as artificial blood vessels (1 dataset) and porcine cadavers (1 dataset). 5.5 Challenges and Opportunities The development of medical video datasets has enabled substantial progress across segmentation, detection, tracking, and registration, yet the field continues to face enduring challenges. Annotation quality. On one hand, generating pixel- or frame-level ground truth requires extensive expert labor, particularly in domains such as surgical tool segmentation and landmark tracking, where precision and temporal consistency are essential [78, 79, 80, 81, 82]. Few datasets except the CaDIS dataset provide fine-grained, frame-level annotations covering full scenes in videos. On the other hand, there may be variations in annotation quality across different videos, even within the same dataset, due to differences in surgeon skill when multiple annotators are involved. Sparse or weak labels have been proposed as a compromise, but they often limit the reliability of downstream evaluation. Semi-supervised and synthetic data augmentation approaches show promise, though their acceptance in clinical research requires rigorous validation. Data privacy. Unlike natural video, medical recordings inherently encode sensitive patient informa- tion. De-identification is particularly challenging in endoscopy and surgery, where anatomical con- text itself can serve as a patient identifier. Consequently, dataset releases are frequently restricted in scale or geographic scope, hampering the establishment of broadly generalizable benchmarks [83, 84, 85]. Addressing this requires technical advances in anonymization as well as standardized regulatory and ethical frameworks that enable secure multi-center data sharing. Domain shift represents a further persistent issue. Substantial variability arises from differences in imaging devices, acquisition protocols, and surgical practices, often causing models trained on one dataset to fail when applied to another. This problem has been observed across lesion detec- tion, artifact removal, and instrument recognition benchmarks [86, 87, 88, 89]. While phantom or synthetic data help isolate algorithmic behavior, bridging these controlled conditions with the com- plexity of real clinical environments remains an open research frontier. Robust domain adaptation, self-supervised pretraining, and benchmark designs that explicitly incorporate cross-institutional variation are therefore pressing needs. Computational burden.From a computational standpoint, the scale of medical video poses formidable demands. High-resolution intraoperative recordings can span hours, making storage, annotation, and real-time analysis resource-intensive. Real-time deployment, for instance in robotic surgery or intraoperative navigation, requires methods that balance accuracy with computational ef- ficiency [90]. Furthermore, emerging benchmarks increasingly combine multiple tasks—detection, segmentation, and tracking—placing pressure on algorithm design to unify spatiotemporal reasoning under constrained latency. These challenges, however, also motivate transformative opportunities. The rise of multimodal datasets such as P2ILF and SAR-RARP50 opens pathways toward comprehensive scene understand- ing, aligning 2D video streams with 3D imaging modalities and enabling clinically relevant multi- modal registration [91, 92]. The integration of large pre-trained models and foundation architectures has the potential to mitigate annotation bottlenecks and improve generalization across institutions, provided that interpretability and domain alignment are addressed. Longstanding community ini- tiatives, such as the EndoVis series challenges, further underscore the importance of standardized evaluation protocols for reproducibility and clinical translation. Clinically, the opportunities are pro- found. Accurate lesion detection and temporal localization can support early diagnosis in screening procedures, while reliable instrument tracking and workflow analysis enable intraoperative decision support and skill assessment [93, 94]. More broadly, the convergence of diverse datasets, robust 43 IDFieldBrief description 1 dataset_nameOfficial name or commonly used short name of the dataset. 2 release_dateFirst public release date (Y-M or Y-M-D; use NA if unknown). 3 homepage_urlStable URL or DOI for the dataset homepage, paper, or repository. 4 organizationInstitution(s) releasing or hosting the dataset; multiple entries allowed, separated by commas. 5 challenge_seriesName of the associated challenge or benchmark series; NA if not challenge-based. 6 licenseData usage license or access policy as specified by the download agreement. 7 dataset_descriptionShort free-text summary of source, modality, tasks, scale, and key characteristics. 8 modality_primaryPrimary imaging modality or modalities (e.g., CT, MR, X-ray, Fundus). 9 modality_secondarySubtype or sequence within the primary modality (e.g., MR:T1, CT:CTA; NA if unspecified). 10 anatomical_structureTarget organ, region, or lesion; multiple structures allowed. 11 diseaseDisease or clinical condition(s) represented; NA for non disease-specific datasets. 12 data_volumeTotal size and split, preferably as JSON (e.g., "total":..., "train":...). 13 valid_image_nUsable sample count after cleaning, optionally in the same JSON format as data_volume. 14 label_presenceAnnotation availability: labeled, unlabeled, or mixed. 15 task_typeSupported computational tasks (e.g., segmentation, detection, classification, VQA). 16 num_classes_per_taskJSON describing, per task, the number of classes/targets and relevant settings. Table 2: Definition of data_meta fields for dataset-level metadata. DatasetSourcePool TCIA TCGA ISIC · ADNI FilterMode1:JSONRules "dimension":"2D", "modalities":["CT","MR"], "task_types":[...], "anatomies":[...], "license":[...], "min_valid_num":1000, ... FilterMode2:DirectSearch Keyword, datasetnames, organization,... DatasetFusion 2.Alignment:aligndataset tasksemanticsandlabels 3.Blueprint:usemetadata toclusterdatasetsand assesspotentialforfusion 4.Indexing:buildpublic indicesandvisualsummaries 1.Harmonization:standardize metadataacrossdatasets DatasetStatistics InteractiveDiscoveryPortal MetadataSummary StatisticsVisualization Tablefor MatchingDatasets Figure 18: Pipeline of our dataset collection, processing, fusion, and summarization system based on the proposed dataset fusion paradigm, implemented in our interactive discovery portal. benchmarking, and advanced learning paradigms is steering medical video analysis from narrow research prototypes toward clinically indispensable technologies. In summary, medical video datasets face inherent challenges in annotation, privacy, domain robust- ness, and scalability, but these limitations are also drivers of innovation. With sustained progress in dataset diversity, federated evaluation, and integration with large-scale learning systems, the field is positioned to deliver clinically impactful solutions in the coming decade. 6 Paradigm for Dataset Fusion Despite the abundance of public medical imaging datasets, their fragmentation significantly hinders their effective use in large-scale model training. To address this, we propose the Metadata-Driven Fusion Paradigm (MDFP), grounded in our comprehensive collection and curation of medical imag- ing datasets, offering an efficient, scalable, and metadata-centric strategy to systematize discovery, 44 BlockRoleRepresentative fields and examples recordSample identifiers and cross-references dataset_name, image_path, optional sample IDs used to locate the un- derlying media file and join annotations across tasks. contextClinical and textual contextSubject- and acquisition-level metadata (e.g., subject ID, age, sex, site, modality, anatomy), extensible extra dictionary, free-text descriptions or reports. media_geometryMedia attributes and geometryMedia-level task configuration and spatio-temporal metadata, including task_type, leaf_task, annotation_type, dimension, pixel spacing, orientation, slice/frame indices, timestamps, camera parameters. tasksTask-specific annotation payloadsStructured labels for the predefined tasks, such as segmentation masks, de- tection boxes, class labels, polygons, or keypoints, grouped by dimension and schema_variant. Table 4: Four logical blocks—record, context, media_geometry, and tasks—structuring annotations_task.jsonl. The blocks separate sample identity, context, media-level geom- etry, and task-specific annotation payloads. auditing, and composition of multiple datasets. By operating primarily on metadata rather than raw pixels, MDFP reduces handling overhead and privacy risk, improves reproducibility and auditability, and enables rapid goal-conditioned dataset assembly. Based on MDFP, we build an interactive dis- covery portal that supports fine-grained dataset search, integration, and statistical analysis. Figure 18 overviews the full system, and Figure 19 details MDFP. The remainder of this section proceeds in the following order: we describe dataset collection and processing (Section 6.1); introduce MDFP and its four phases (Section 6.2); incorporate the afore- mentioned components to form the interactive discovery protal (Section 6.3). 6.1 Dataset Collection and Processing All datasets included in this study were obtained from publicly accessible web-based repositories, such as The Cancer Imaging Archive (TCIA) 5 , Grand Challenge 6 , OpenNeuro 7 , Kaggle 8 , Neu- roImaging Tools and Resources Collaboratory (NITRC) 9 , Synapse 10 , CodaLab 11 , GitHub 12 , etc. After collecting the medical imaging datasets from these sources, we organize them into a multi- dimensional database that serves as a comprehensive overview table. This database categorizes each dataset by multiple attributes, including dimension, modality, anatomical structure, number of cases, label availability, and task type, along with other essential metadata. Such an organization enables flexible querying and filtering, allowing researchers to quickly retrieve datasets that match specific research needs, i.e., training a 3D foundation model for CT, MRI, and PET. For each individual dataset, we preserve the original directory layout as much as possible and en- rich it with two JSONL files. The file data-meta.jsonl records dataset-level information such as release date, imaging modality, homepage URL, and license, using 16 well-defined fields (summa- rized in Table 2). The file annotations-task.jsonl stores per-media, task-specific annotations (for example, mask file paths for segmentation tasks or bounding boxes for detection tasks). Each JSON object describes a single annotated media item for a particular task and is decomposed into four logical information blocks: record, context, media_geometry, and tasks. The record block contains stable identifiers such as dataset_name (dataset identifier) and image_path (path or key of the underlying image, volume, or video file within that dataset), which link the annota- tion back to the original media file and allow annotations for the same sample to be joined across tasks. The context block collects optional subject- and acquisition-level metadata and free-text descriptions (e.g., subject ID, age, sex, site, modality, anatomy, and an extensible extra dictio- nary for dataset-specific fields). The media_geometry block captures media-level attributes and 5 https://w.cancerimagingarchive.net 6 https://grand-challenge.org 7 https://openneuro.org 8 https://w.kaggle.com/ 9 https://w.nitrc.org 10 https://w.synapse.org 11 https://codalab.lisn.upsaclay.fr 12 https://github.com 45 Target task types: [classification, segmentation, detection, regression]; License allowlist: none. include unlabeled: yes; prefer labeled in selection: yes; min valid_image_n: per dataset: 100. Selection enabled: False (min_datasets_per_modality=2, min_orgs_per_modality=2) ... Goal:Pretrain a 2D medical foundation model over modalities [CT, MR, Fundus]. Refinement MDFP Process Phase 1: Harmonisation Phase 2: Alignment Phase 3: Blueprint Phase 4: Indexing Input Curated Database Output modalityn_datasetssum_imagen_orgslabeled CT10117396541 Fundus42280311170.95238 MR568102521 Figure 19: Detailed process of the proposed metadata-driven fusion paradigm (MDFP). spatial/temporal geometry that are shared by all annotations on the same media item, including the high-level task_type (one of 12 predefined task categories such as segmentation, detection, or clas- sification), the dataset-specific leaf_task, the annotation_type (e.g., binary masks, bounding boxes, polygons), the dimension (2D, 3D, or video), and imaging metadata such as pixel spacing, orientation, slice or frame indices, timestamps, and camera parameters. Finally, the tasks block contains the structured task-specific annotation payloads themselves (e.g., mask references, box co- ordinates, class labels, keypoints), organized in a schema that is consistent across datasets for the same task type. A compact overview of these four blocks and their representative fields is given in Table 4. Building on the standardized directory structure, we further unify file formats with a focus on pre- serving quantitative information and metadata. Specifically, all volumetric imaging data (e.g., CT, MRI, PET) are converted to NIfTI (.nii.gz), with voxel spacing, orientation (qform/sform), and in- tensity scaling (slope/intercept or equivalent) preserved; dynamic PET is stored as 4D NIfTI with companion JSON/TSV files for frame timing and calibration (e.g., SUV factors). When appropriate, de-identified source DICOMs are retained as an optional raw layer. For 2D modalities (e.g., radio- graphs, ultrasound frames), we preserve full dynamic range using lossless 16-bit formats (TIFF or 16-bit PNG) together with sidecar JSON for essential metadata (pixel spacing, orientation, window settings, modality-specific tags). For video data (e.g., endoscopy, ultrasound cine, surgical record- ings), we store sequences in compressed formats (MP4 or AVI with H.264/H.265 encoding) while maintaining 8-bit color depth for most clinical videos; specialized applications requiring higher dynamic range (e.g., fluorescence microscopy, high-speed recordings) are preserved as 16-bit se- quences when available. Video metadata, including frame rate, resolution, acquisition timestamps, and equipment parameters, are recorded in companion JSON files to ensure reproducibility and temporal consistency. Optional 8-bit PNG thumbnails may be generated solely for visualization and documentation; these are never used as training inputs when quantitative intensity matters. This preprocessing and standardization workflow maintains dataset fidelity and compatibility, facilitates seamless integration into model-training pipelines, and enables reproducible and comparable bench- marking across studies. 6.2 MDFP MDFP systematizes discovery, auditing, and composition through four sequential phases that oper- ate primarily on structured metadata, strengthening privacy, auditability, and reproducibility while avoiding raw-pixel handling. Table 5 presents these phases of MDFP, outlining their core objectives and associated metadata fields. These phases are tightly aligned with our systematic metadata collection framework, ensuring 46 Table 5: MDFP Workflow Overview with Key Metadata Fields PhaseObjectiveMetadata Fields 1. Harmoniza- tion Standardize modalities,tasks,and anatomy. modality_primary, dimension, anatomical_structure, organization, challenge_series 2. AlignmentAlign semantic labels and tasks across datasets. task_type, modality_secondary, label_presence, notes 3. BlueprintCluster datasets; assess integrative po- tential and data scale. data_volume, valid_image_n, storage_size_gb 4. IndexingCreate public metadata indices and vi- sualization tools for easy access. dataset_name, release_date, homepage_url, license consistency, completeness, and interoperability across heterogeneous datasets. Below, we detail each phase. 6.2.1 Phase 1: Metadata Harmonization Phase 1 resolves semantic heterogeneity by enforcing a rigorously defined metadata schema. Rather than creating a new vocabulary, we ground our schema in authoritative medical terminologies, such as the Unified Medical Language System (UMLS) and Medical Subject Headings (MeSH) [95]. This process is semi-automated, leveraging API-driven searches against these ontologies, followed by LLM-based refinement to programmatically align disparate dataset descriptors into a consistent, machine-readable form. Concretely, we: • Standardize primary modality (modality_primary): Mapped to an enumerated set in- cluding CT, MR, PET, US, X-ray, etc., with niche modalities deterministically aligned to this taxonomy. • Normalize data dimensionality (dimension): Parsed directly from dataset metadata to determine whether the data is 2D, 3D, or video (2D + time). • Establish hierarchical classification: Instead of a simple anatomical tag, we implement a multi-level classification system based on standard medical ontologies (e.g., UMLS, MeSH). This provides rich, hierarchical context. For example, a dataset on cataracts would be classified under Eye Diseases→ Lens Diseases→ Cataract. • Record provenance and context (organization, challenge_series): Identifying the originating institution and any associated benchmark or competition series. • Document annotation granularity (annotation_type): Explicitly cataloging the type and granularity of labels provided, including landmark coordinates, pixel-level segmenta- tion masks, region-level bounding boxes, image-level classification labels, or multi-modal annotations. This metadata enables researchers to identify datasets compatible with their task requirements and facilitates appropriate fusion strategies during retrieval. This harmonization step yields a uniform, richly annotated metadata table that transforms a frag- mented corpus into an interoperable resource, thereby enabling reliable cross-dataset comparison, reproducible filtering, and seamless integration. 6.2.2 Phase 2: Semantic Alignment Phase 2 mitigates inconsistencies by mapping abstract machine learning tasks to their concrete clin- ical significance. This crucial step involves a systematic review of dataset documentation, source publications, and official guidelines to understand the intended real-world application. By doing so, we align heterogeneous labeling conventions and evaluation objectives with tangible clinical goals. Specifically, we: • Define downstream tasks (downstream_task): We standardize ML tasks and explicitly map them to their clinical applications. For example: 47 – A classification task might correspond to clinical diagnosis (e.g., malignant vs. benign tumor), severity grading (e.g., staging diabetic retinopathy), or treatment re- sponse prediction. – A segmentation task may be used for lesion delineation (e.g., outlining a tumor boundary), volumetric quantification (e.g., measuring organ volume to track disease progression), or radiotherapy/surgical planning. – A detection task is often used for disease screening (e.g., identifying candidate pulmonary nodules in a chest CT). – A regression task can quantify clinical biomarkers (e.g., predicting bone mineral density from a CT scan or cardiac ejection fraction from an ultrasound video). • Indicate label availability (label_presence): Denoting whether ground-truth annota- tions are provided (labeled) or not (unlabeled). • Specify secondary imaging modalities (modality_secondary): Capturing finer-grained protocol-level distinctions under each primary modality, such as T1 or T2 sequences for MR. • Document special considerations (notes): Capturing dataset-specific nuances, assump- tions, or known limitations in free-text form. This alignment phase yields a clinically-grounded task vocabulary that supports meaningful inter- pretation, goal-oriented filtering, and enhances the reliability of cross-dataset benchmarking. 6.2.3 Phase 3: Fusion Blueprints This phase leverages harmonized metadata to design strategic dataset integration plans. Specifically, we perform grouping and categorization based on combinations of primary and secondary imag- ing modalities (modality_primary, modality_secondary), clinical tasks (task_type), and anatomical coverage (anatomical_structure). This grouping process consolidates datasets with similar or identical attributes into unified groups, providing a structured foundation for designing fu- sion blueprints that guide principled dataset integration. Quantitative evaluations are systematically derived from metadata, encompassing the following aspects: • Data Volume (data_volume): Assess total images available, along with explicit training, validation, and testing splits. • Valid Image Counts (valid_image_n): Determine precisely how many images have re- liable and validated annotations, critical for training supervised models. These quantitative statistics are obtained directly from official dataset documentation, README files, pub- lished papers, and other authoritative public resources provided by dataset curators. • Storage Estimation (storage_size_gb): Evaluate practical storage requirements, essen- tial for infrastructure planning. • Anatomical and Task Diversity (anatomical_structure, task_type): Quantify anatomical breadth and task variety within each fusion cluster, ensuring coverage diver- sity crucial for generalization. This structured assessment produces a principled basis for scalable dataset merging, balancing quan- tity, annotation quality, and content diversity to support robust foundation model training. Dur- ing fusion blueprint design, we explicitly account for data heterogeneity captured in Phase 1, in- cluding variations in imaging protocols (e.g., differences in CT reconstruction parameters or MRI field strengths), image resolutions, and annotation granularities. Our tool systematically identi- fies and flags datasets with incompatible annotation types (e.g., mixing pixel-level segmentation with bounding-box detection) and imaging protocol differences, alerting researchers to potential in- tegration challenges. These metadata annotations help researchers make informed decisions about whether harmonization preprocessing, protocol-aware sampling strategies, or domain-adaptive train- ing approaches are needed to ensure cross-dataset compatibility and model robustness. 48 Table 6:MDFP-derived composition for the 2D CT/MR/Fundus goal. sum_image is sum(valid_image_n); labeled_ratio is the fraction of datasets that are labeled. modality n_datasets sum_image n_orgs labeled_ratio CT101,173,96541.000 MR5681,02521.000 Fundus42280,311170.952 6.2.4 Phase 4: Dataset Indexing and Community Sharing Phase 4 transforms the harmonized metadata into a structured, publicly accessible dataset index to support community-scale discovery and reuse. We consolidate key metadata elements for each dataset, including: • Dataset name (dataset_name): the canonical name of the dataset for standardized refer- encing; • Release date (release_date): official publication or release timestamp, enabling tempo- ral filtering; • Homepage URL (homepage_url): direct access link to dataset documentation or hosting platform; • License (license): clearly defined usage permissions, ensuring legal compliance and re- producibility. This indexed representation facilitates rapid dataset discovery, promotes responsible reuse, and pro- vides the infrastructure foundation for large-scale model pretraining, benchmarking, and open col- laboration. 6.2.5 Case Study: Goal-Conditioned Fusion via MDFP As shown in Figure 19, to demonstrate how MDFP supports foundation-model pretraining with reproducible, goal-aligned data composition, we instantiate a concrete target: a 2D model over modalities CT, MR, Fundus and tasks classification, segmentation, detection, regression. In Phases 1–2 (Harmonization and Alignment), we apply the following filtering and standardization procedures: First, we standardize all datasets by mapping primary modalities to our controlled vo- cabulary (CT, MR, Fundus) and normalize data dimensionality to 2D only, explicitly excluding 3D volumetric data and video sequences to maintain dimensional consistency. Second, we establish hi- erarchical anatomical classifications using UMLS/MeSH ontologies for each dataset, enabling con- sistent cross-dataset anatomical mapping. Third, we perform semantic alignment by mapping ma- chine learning task types to their clinical applications, ensuring that selected datasets cover the four target task families: classification (diagnosis, severity grading), segmentation (lesion delineation, volumetric quantification), detection (disease screening), and regression (clinical biomarker quan- tification). Fourth, we apply quality filters including minimum sample size (valid_image_n≥ 100) to ensure statistical reliability. Both labeled and unlabeled datasets are retained, with preference given to labeled ones when multiple alternatives exist. No license restrictions are enforced in this demonstration, though license-aware filtering is supported by the framework. In Phases 3–4 (Blueprint and Indexing), we perform grouping and categorization based on the har- monized metadata from Phases 1–2. Specifically, we group datasets by modality-task combinations, assess the integrative potential of each cluster by quantifying data volume, annotation availability, and anatomical coverage, and generate a fusion blueprint that summarizes the composition strategy. Finally, we create a structured metadata index with all essential fields (dataset_name, modality, task, valid_image_n, license, homepage_url) to enable reproducible access and community sharing. The resulting integrated dataset composition is summarized in Table 6. The curated pool com- prises 57 datasets and 2,135,301 validated images across three imaging modalities: CT (10 datasets, 1,173,965 images, 4 organisations), MR (5 datasets, 681,025 images, 2 organisations), and Fundus (42 datasets, 280,311 images, 17 organisations). These numbers correspond to the summary fields 49 n_datasets, sum_image (defined as P valid_image_n), n_orgs, and labeled_ratio in Ta- ble 6. All CT and MR datasets are fully annotated (labeled_ratio=1.000), and Fundus datasets achieve a high annotation rate (labeled_ratio=0.952), yielding strong supervision across modal- ities while satisfying the case-study constraints (2D only; CT/MR/Fundus) and covering the four target task families defined in Phase 2 (classification, segmentation, detection, regression). This configuration constitutes a concrete instantiation of goal-conditioned dataset integration via MDFP. These aggregate statistics have direct implications for foundation-model pretraining. The high la- beled fractions support multi-task supervised objectives, while the remaining unlabeled images can be exploited with auxiliary self-supervised losses. At the same time, the skew in sum_image to- ward CT and MR (CT+MR≈ 1.85 M images) suggests employing modality-aware sampling strate- gies (e.g., temperature-based sampling or per-dataset caps) and task-stratified batching to prevent over-representation of these modalities. Finally, although this case study restricts sources to 2D CT/MR/Fundus datasets for clarity, the same MDFP pipeline can be rerun with relaxed configura- tion (e.g., enabling allow_3d_as_2d_sources=true) to augment the 2D pool with projected 3D or video data when broader coverage is desired. 6.3 Interactive Discovery Portal Combining the aforementioned components together, we build a lightweight interactive discovery portal, namely the Medical Dataset Browser, to triage and refine candidate datasets before schema- level alignment. The portal is deployed as a single page static application on GitHub Pages 13 , exe- cutes entirely client-side, and consumes at runtime the standardized JSON artifact produced in 6.1 (for example, the cleaned and merged manifest). This design eliminates server-side dependencies, simplifies reproducibility, and enables privacy-preserving exploration. Below, we detail the pipeline. Dataset Filtering. Starting from the dataset source pool prepared in Section 6.1, the portal exposes two complementary modes for dataset filtering. First, Rule-based filtering (“Filter Mode 1”). This mode implements the MDFP, accepting an editable JSON specification that encodes deterministic selection criteria, e.g., image dimension (2D/3D), modality sets (CT/MRI/US/Pathology, etc.), task types (segmentation, detection, classification, report generation), organ/anatomy whitelists, license constraints, minimum sample sizes, and year ranges. This recipe-like abstraction makes selections auditable and perfectly reproducible. Specifically, the controls for Phase 1&2 in MDFP (harmonization and alignment) and Phase 3&4 (blueprint and indexing) are integrated into the page to preview downstream effects before commit- ting a batch run. During execution, the interface highlights in-progress elements and then surfaces consolidated outputs for inspection. In parallel, direct faceted search (“Filter Mode 2”) provides dropdown facets and a free text query for fast exploratory narrowing. Both modes drive live visual summaries, complete bar and dough- nut charts of dimension-modality-task distributions, so users can immediately assess coverage and balance of the current subset. Statistics and summaries. After the dataset filtering, the portal renders live bar/pie summaries of modality, dimension, task, and anatomy distributions; a MDFP Phase-4 audit table exposes fields essential for screening and compliance: name, dimension, modality, task, organ, images (counts), year, organization, license, and link. These statistics and summaries can be ex- ported to CSV/JSON for benchmarking pipelines. Together, these views close the loop from search to fusion to shareable artifacts. Implementation details. The index.html bootstraps by loading the preprocessed JSON mani- fest, initializes an in-memory filter store, and applies deterministic, order-independent rule evalua- tion entirely on the client. Visual analytics and tables are rendered with lightweight, dependency- minimal components; results are paginated to maintain interactivity on medium-to-large corpora. Because the application is a self-contained static bundle, any user can fork, reconfigure the selection recipe, and redeploy an identical retrieval environment without additional infrastructure. 13 https://tchenglv520.github.io/medical-dataset-browser/ 50 7 Discussion 7.1 Limitations in Task Definition and Evolution of Data Engineering Paradigms Current open-access medical imaging datasets exhibit limitations in task definition, reflecting the task-oriented nature of early deep learning practices [96, 11]. Most datasets target indirect down- stream tasks (e.g. segmentation, classification, or detection), which served as proxies for clinical goals but remain distant from real-world applications. With the advancement of foundation models and LLMs, AI systems are shifting toward direct, clinically relevant tasks such as disease diagno- sis, patient condition assessment, and treatment recommendation [97]. This paradigm shift creates a critical mismatch: existing datasets, designed for classical computer vision tasks, cannot be di- rectly utilized without substantial transformation. However, re-annotation for clinically oriented tasks incurs prohibitively high costs, as these tasks demand expert-level medical knowledge and high-quality annotations from domain specialists. For instance, a lung nodule segmentation mask does not indicate whether the nodule is benign, malignant, or requires biopsy—information essential for clinical decision support but absent in existing annotations. Bridging this gap requires resource-intensive re-annotation by radiologists, a process that scales poorly across large datasets. Consequently, the medical AI community faces a dual challenge: legacy datasets are misaligned with contemporary needs, yet creating new foundation-model-ready datasets remains economically and logistically prohibitive. Future data engineering must priori- tize flexible annotation frameworks that capture clinically meaningful information upfront, enabling adaptation to evolving AI paradigms without complete re-annotation. 7.2 Scarcity of Multimodal Medical Datasets and Constraints in Further Development Multimodal medical data that integrates imaging modalities (CT, MRI, pathology) with clinical re- ports, genomics, and temporal records holds exceptional value for clinical diagnosis, yet remains exceedingly rare in the public domain [11]. Most open-access datasets are unimodal and lack stan- dardized frameworks for multimodal collection and annotation [98, 99], significantly restricting research in cross-modal reasoning and joint representation learning essential for next-generation medical AI. The challenge extends beyond data availability to fundamental issues of modal align- ment and semantic consistency. Different modalities operate on disparate scales: pathology captures microscopic cellular details, radiology visualizes organ-level structures, and clinical notes document temporal disease progression. Harmonizing these heterogeneous streams requires sophisticated alignment protocols and cross- modal validation standards that current datasets rarely provide. For example, aligning a radiologist’s report timestamp with the corresponding imaging study, or synchronizing pathology findings with longitudinal treatment records, demands metadata infrastructure largely absent in existing resources. Moreover, the absence of standardized multimodal benchmarks impedes systematic evaluation of cross-modal architectures. Researchers lack unified frameworks to assess whether models effec- tively integrate complementary information across modalities or leverage modal-specific strengths to compensate for individual limitations. This evaluation gap slows development of clinically viable systems capable of synthesizing diverse diagnostic information as human clinicians do. The techni- cal complexity of multimodal data management compounds these challenges. Institutions struggle with storage, versioning, and synchronization of large-scale heterogeneous datasets, while privacy regulations complicate cross-institutional data sharing. Without robust infrastructure and standard- ized curation protocols, the field remains fragmented, with isolated efforts failing to achieve the critical mass needed for breakthrough advances in multimodal medical AI. 7.3 Challenges and Opportunities in Medical Foundation Models Medical foundation models demand unprecedented scale and diversity in training data, yet current resources remain insufficient for developing truly generalizable systems [100, 101, 102, 103]. The gap between available data and foundation model requirements is particularly evident in specialized domains such as pediatric imaging, rare diseases, and longitudinal treatment monitoring. Three interconnected challenges fundamentally constrain progress in this field. Scale and Representational Diversity. Beyond sheer quantity, foundation models require compre- hensive coverage across disease presentations, imaging protocols, clinical specialties, and patient 51 demographics to develop robust internal representations. Current medical datasets typically capture narrow slices of clinical reality, missing the long-tail distribution of rare conditions and atypical presentations that characterize real medical practice. This limitation is especially acute in underrep- resented populations and emerging disease variants. Licensing and Privacy Constraints. Unlike general-domain AI where datasets can be freely shared, medical data faces dual constraints from patient privacy regulations (e.g., HIPAA, GDPR) and institutional intellectual property policies. Even when foundation models can generate high- quality synthetic data for training augmentation [104, 81], restrictive licensing prevents these en- hanced datasets from benefiting the broader research community. This regulatory landscape frag- ments the field, forcing redundant efforts across institutions and limiting collaborative progress [105]. Contextual and Temporal Intelligence. Effective medical AI must transcend pattern recognition to understand clinical workflows, resource constraints, and patient-specific contexts [106]. For in- stance, models must distinguish between emergency protocols and routine screening, interpret how prior treatments influence current presentations, and track disease progression over time. Current training paradigms inadequately address these temporal reasoning and workflow integration capa- bilities essential for real-world deployment. Addressing these challenges requires coordinated efforts to establish data governance frameworks that balance privacy protection with research advancement. Without systemic solutions—including federated learning infrastructures, standardized licensing models, and clinically-grounded evalua- tion benchmarks—medical foundation models will remain confined to narrow applications rather than achieving the general intelligence needed for transformative clinical impact. 8 Conclusion This comprehensive survey of over 1,000 open-access medical image datasets reveals a fragmented and imbalanced landscape that fundamentally constrains the development of medical foundation models. Existing datasets remain predominantly small-scale, task-specific, and modality-restricted, with pronounced disparities across anatomical regions and imaging modalities. These limitations reflect the field’s incomplete transition from task-oriented to foundation-oriented data engineer- ing paradigms. To address these challenges, we formulate the Metadata-Driven Fusion Paradigm (MDFP), a systematic framework for dataset integration that enables the construction of larger, more diverse training resources essential for foundation model development. Our analysis identifies three critical gaps: the scarcity of multimodal datasets that limits cross-modal reasoning capabilities, re- strictive licensing and privacy regulations that fragment collaborative efforts, and the absence of contextual intelligence necessary for real-world clinical deployment. The dominance of segmenta- tion and classification tasks, alongside the underrepresentation of emerging applications like visual question answering and multimodal reasoning, underscores the urgent need for comprehensive data engineering strategies. Looking forward, advancing the development of medical foundation models requires a collective shift toward openness, efficiency, and inclusivity in data engineering. A key priority should be to encourage broader public release of medical imaging datasets, thereby enhancing transparency, re- producibility, and equitable access across institutions and regions. In parallel, research on synthetic data generation holds promise for mitigating privacy and data scarcity challenges, while annotation- efficient learning approaches can enable effective use of partially labeled or weakly supervised data. Moreover, the public release of foundation models trained on private or institution-specific data, even when raw datasets cannot be shared, represents a practical pathway to democratize access to advanced medical AI capabilities. Together, these strategies constitute a sustainable and collabora- tive framework for building truly generalizable and clinically impactful medical foundation models. 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URL http://dx.doi.org/10.3390/app11020672. 157 A Tables of 2D Medical Image Datasets B Tables of 3D Medical Image Datasets 131 Table 7: 2D CT datasets. #DatasetYearDimModalityStructureImagesLabelTaskDiseases 1LoDoPaB-CT [107]20202DCTLung28YesReconNA 2 5K+ CT Images on Fractured Limbs [108] 20212DCTLimbs24YesSegBone Fracture 3AREN0534 [109]20213D, 2DMulti a Kidney, Lung239NoEstKidney Tumor 4CT Medical Images [110]20172DCTLung475YesSegNA 5National Lung Screening Trial [111]20133D, 2DCT, PathologyLung26.7kNoClsLung Cancer 6 RSNA Intracranial Hemorrhage Detection [112] 20192DCTBrain874kYesLocIntracranial Hemorrhage 7CT diagnosis of COVID-19 [113]20212DCTLung275YesClsLung COVID-19 8COVID-19-CT SCAN IMAGES [114]20212DCTLung1.4kYesClsLung COVID-19 9COVID_CT_COVID-CT [113]20212DCTLung746YesClsLung COVID-19 10Chest CT-Scan images Dataset [115]20212DCTLung1kYesClsLung Cancer 11Cranium Image Dataset [116]20202DCTBrain50YesDetIntracranial Hemorrhage 12SARS-COV-2 Ct-Scan Dataset [117]20212DCTLung2.5kYesClsLung Disease 13MedMNIST [118]20202D Multi b Retina, Breast, Lung 100kYesClsMulti-disease 14The Visible Human Project [119]19943D, 2DCT, MR, etc.Full Body2NoNASkin Lesion 15ImageCLEF 2016 [120]20152DMulti c Skin, Cell, Breast31kYesClsHead & Neck Tumor 16RadImageNet (Subset: CT) [121]20222DCTFull Body292.4kYesCls Abdomen, lung, etc. d 17 Brain CT Images with ICH Masks [116] 20192DCTBrain82YesSegIntracranial Hemorrhage 18CMB-CRC [122]20223D, 2DMulti e Colon472NoSeg, ClsColorectal Cancer 19CMB-GEC [123]20223D, 2DCT, WSI, PETBrain14NoSeg, ClsMelanoma 20CMB-MEL [124]20223D, 2D Multi f Brain255NoSegMelanoma 21CMB-MML [125]20212D, 3DMulti g NA60NoNAMultiple Myeloma 22CMB-PCA [126]20222D, 3DCT, MR, WSIProstate31NoCls, PredProstate Cancer 23CPTAC-LSCC_CT_PET [127]20182D, 3D CT, PET, Histopathology NA238NoNANA 24 Finding and Measuring Lungs in CT Data [128] 20192D, 3DCTLung534YesSegNA 25Head CT Image Data [129]20192DCTHead200YesClsNA 26LDCTIQAC2023 [130]20232DCTNA1kYesRegNA 27APOLLO-5 [131]20222D, 3D Multi h NA6.2kNoNANA 28Lung-Fused-CT-Pathology [132]20182D, 3D CT, Histopathology Lung36YesSegLung Disease 29CMB-LCA [133]20222D, 3D Multi i NA0NoNANA 30RIDER Phantom PET-CT [134]20112DCT, PETNA2.2kNoNANA 31AHOD0831 [135]20223D, 2D Multi j NA0NoNAHodgkin Lymphoma 32Prostate-MRI [136]20113D, 2D Multi k Prostate26NoNAProstate Cancer 33AREN0532 [137]20223D, 2D Multi l NA1kNoNAWilms Tumor 34ImageCLEF 2016 (Duplicate) [120]20152DMulti c Skin, Cell, Breast31kYesClsHead & Neck Tumor 35QUBIQ2020 [138]20202DCT, MR Kidney, Pancreas, etc. 150YesSegNA 36QUBIQ2021_2D_CT [139]20212DCT, MR Kidney, Pancreas, etc. 268YesSegNA Overall1994∼20222DMultiFull Body1.4mNAMultiMulti a Multi-modalities of AREN0534: CT, MR, PET, Ultrasound. b Multi-modalities of MedMNIST: OCT, X-Ray, CT, Pathology, Fundus Photography. c Multi-modalities of ImageCLEF: MR, US, Histopathology, X-Ray, CT, PET, Endoscopy, Dermoscopy, EEG, ECG, EMG, Microscopy, Fundus. d The complete list of diseases for RadImageNet includes: prostate lesion, adrenal pathology, gallstone, arterial pathology, urolithiasis, pancreatic lesion, etc. e Multi-modalities of CMB-CRC: CT, MR, US, X-ray, PET, WSI. f Multi-modalities of CMB-MEL: CT, US, WSI, PET. g Multi-modalities of CMB-MML: CT, MR, PET, WSI. h Multi-modalities of APOLLO-5: CT, MR, US, PET, X-Ray. i Multi-modalities of CMB-LCA: CT, MR, US, Histopathology, X-ray. j Multi-modalities of AHOD0831: CT, MR, PET, X-Ray. k Multi-modalities of Prostate-MRI: MR, CT, PET, Pathology. l Multi-modalities of AREN0532: CT, MR, Ultrasound, PET. Abbreviations: Cls=Classification, Det=Detection, Est=Estimation, Histo=Histopathology, Loc=Localization, Pred=Prediction, Recon=Reconstruction, Reg=Registration, Seg=Segmentation, US=Ultrasound, WSI=Whole-slide images. 132 Table 8: 2D MRI datasets. #DatasetYearDimModalityStructureImagesLabelTaskDiseases 37AREN0534 [109]20212D, 3DMulti a Kidney, Lung239NoEstKidney Tumor 38KNOAP2020 [140]20202D, 3DMR, X-RayKnee30YesPredOsteoarthritis 39braimMRI [141]20222DMRBrain110YesSegBrain Tumor 40Brain-MRI [142]20202DMRBrain110YesSegBrain Disease 41SpinalDisease2020 [143]20202DMRSpine150YesDetSpinal Disease 42The Visible Human Project [119]19942D, 3D CT, MR, Others Full Body2NoNASkin Lesion 43ImageCLEF 2016 [144]20152D Multi b Skin, Cell, Breast31kYesClsH&N Tumor 44CMB-CRC [145]20222D, 3DMulti c Colon472NoSeg, ClsColorectal Cancer 45CMB-MML [146]20212D, 3D Multi d NA60NoPredMultiple Myeloma 46CMB-PCA [126]20222D, 3DCT, MR, HistoProstate31NoCls, PredProstate Cancer 47ICDC-Glioma (GLIOMA01)_3D-MR20212D, 3DMR, HistoNA650NoNAGlioma 48Prostate Fused-MRI-Pathology [147]20162D, 3DMR, HistoProstate (Pelvis)29NoNAProstate Cancer 49Cardiac Atrial Images [148]20202DMRAtrium8kYesSegCardiac Disease 50APOLLO-5 [149]20222D, 3DMulti e NA6.2kNoNANA 51CMB-LCA [146]20222D, 3D Multi f NA0NoNALung Cancer 52AHOD0831 [135]20222D, 3DMulti g NA0NoNAHodgkin Lymphoma 53Prostate-MRI [150]20112D, 3D Multi h Prostate26NoNAProstate Cancer 54AREN0532 [137]20222D, 3D Multi i NA1kNoNAWilms Tumor 55ImageCLEF 2015 [151]NA2D, 3D Multi j Skin, Cell, Breast0YesClsNA 56RadImageNet (Subset: MR) [121]20222DMRFull Body673kYesClsWhole Body Abnorm. 57QUBIQ2020 [138]20202DCT, MR Kidney, etc. k 150YesSegPathologies 58QUBIQ2021_2D_MR [139]20212DCT, MR Kidney, etc. k 268YesSegPathologies Overall1994∼20222DMultiFull Body721.5kNAMultiMulti a Multi-modalities of AREN0534: CT, MR, PET, US. b Multi-modalities of ImageCLEF 2016: MR, US, Histo, X-Ray, CT, PET, Endo, Dermo, EEG, ECG, EMG, Micro, Fundus. c Multi-modalities of CMB-CRC: CT, MR, US, X-Ray, PET, Histo. d Multi-modalities of CMB-MML: CT, MR, PET, Histo. e Multi-modalities of APOLLO-5: CT, MR, US, PET, X-Ray. f Multi-modalities of CMB-LCA: CT, MR, US, Histo, X-Ray. g Multi-modalities of AHOD0831: CT, MR, PET, X-Ray. h Multi-modalities of Prostate-MRI: MR, CT, PET, Histo. i Multi-modalities of AREN0532: CT, MR, US, PET. j Multi-modalities of ImageCLEF 2015: MR, US, Histo, X-Ray. k etc. in QUBIQ Structures: Pancreas, Brain, Prostate. Abbreviations: Seg=Segmentation, Det=Detection, Cls=Classification, Pred=Prediction, Est=Estimation; Histo=Histopathology, US=Ultrasound, Endo=Endoscopy, Dermo=Dermoscopy, Micro=Microscopy, Abnorm.=Abnormalities, H&N=Head & Neck. Table 9: 2D PET datasets. #DatasetYearDimModalityStructureImagesLabelTaskDiseases 59AREN0534 [109]20213D, 2DMulti a Kidney, Lung239NoEstKidney 60ImageCLEF 2016 [120]20152DMulti b Skin, Cell, Breast31kYesClsH&N Tumor 61CMB-CRC [122]20223D, 2DMulti c Colon472NoSeg, Cls Colorectal Cancer (H&E stained tissue) 62CMB-GEC [123]20223D, 2DCT, WSI, PETBrain14NoSeg, Cls Melanoma (Cerebral microbleeds) 63CMB-MEL [124]20223D, 2DMulti d Brain255NoSeg Melanoma (Cerebral microbleeds) 64CMB-MML [125]20212D, 3DMulti e NA60NoPredMultiple Myeloma 65CPTAC-LSCC_CT_PET [127]20182D, 3DCT, PET, HistoNA238NoNANA 66APOLLO-5 [131]20222D, 3DMulti f NA6.2kNoNANA 67RIDER Phantom PET-CT [134]20112DCT, PETNA2.2kNoNANA 68AHOD0831 [135]20223D, 2DMulti g NA0NoNAHodgkin Lymphoma 69AREN0532 [137]20223D, 2DMulti i NA1kNoNAWilms Tumor Overall2011∼20222DMultiFull Body41.7kNAMultiMulti a Multi-modalities of AREN0534: CT, MR, PET, Ultrasound. b Multi-modalities of ImageCLEF 2016: MR, US, Histo, X-Ray, CT, PET, Endo, Derm, EEG, ECG, EMG, Microscopy, Fundus. c Multi-modalities of CMB-CRC: CT, MR, US, DX, PET, WSI. d Multi-modalities of CMB-MEL: CT, US, WSI, PET (SWI). e Multi-modalities of CMB-MML: CT, MR, PET, WSI. f Multi-modalities of APOLLO-5: CT, MR, US, PET, X-Ray. g Multi-modalities of AHOD0831: CT, MR, PET, X-Ray. h Multi-modalities of Prostate-MRI: MR, CT, PET, Patho. i Multi-modalities of AREN0532: CT, MR, US, PET. Abbreviations: Seg=Segmentation, Cls=Classification, Est=Estimation, Pred=Prediction, H&N=Head & Neck, US=Ultrasound, Histo=Histopathology, Patho=Pathology, Endo=Endoscopy, Derm=Dermoscopy, WSI=Whole-slide Images, DX=Digital Radiography. 133 Table 10: 2D Ultrasound datasets. #DatasetYearDimModalityStructureImagesLabelTaskDiseases 70HC18 [152]20182DUSSkull1.3kYesMeasNA 71BUSI [153]20192DUSBreast647YesSegBreast Cancer 72APOLLO-5 [149]20222D, 3DMulti a NA6.2kNoNANA 73CMB-LCA [146]20222D, 3DMulti b NA0NoNANA 74ImageCLEF 2015 [151]20152D, 3DMulti c Skin, Cell, Breast0YesClsNA 75ImageCLEF 2016 [120]20162DMulti d Skin, Cell, Breast31kYesClsHead & Neck Tumor 76RadImageNet (Subset: US) [121]20222DUSFull Body390kYesClsAbdominal Structures 77BreastMNIST [153]20212DUSBreast156YesClsBreast Cancer 78AREN0534 [109]20212D, 3DMulti e Kidney, Lung239NoEstKidney Tumor 79CLUST15 [154]20152DUSLiver34YesTrackNA 80Ultrasound Nerve Segmentation [155]20162DUSBrachial Plexus11.3kYesSegNA 81TN-SCUI2020 [156]20202DUSThyroid3.6kYesSegLeukemia 82ImageCLEF 2016 [157]20152DMulti f Skin, Cell, Breast31kYesClsHead & Neck Tumor 83CMB-CRC [145]20222D, 3DMulti g Colon472NoSeg, ClsColorectal Cancer 84CMB-MEL [158]20222D, 3DMulti h Brain255NoSeg Melanoma, Cerebral microbleed 85PSFHS [159]20232DUSNA4.7kYesSegNA 86USenhance2023 [160]20232DUSNA1.5kYesReconNA 87AREN0532 [137]20222D, 3DMulti i NA1kNoNAWilms Tumor 88TN3K [161]20212DUSHead and Neck3.5kYesSegThyroid Nodules 89CAMUS [162]20192DUSHeart1.8kYesSegCardiac Disease 90DDTI [163]20202DUSThyroid637YesSegThyroid Nodule 91UDIAT-B [164]20172DUSBreast163YesDetBreast Lesion 92OASBUD [165]20172DUSBreast200YesSegBreast Cancer 93BrEaST [166]20242DUSBreast256YesClsBreast Cancer Overall2015∼20242DMultiFull Body490.0kNAMultiMulti a Multi-modalities of APOLLO-5: CT, MR, US, PET, X-Ray. b Multi-modalities of CMB-LCA: CT, MR, US, Histo, DX (WSI). c Multi-modalities of ImageCLEF 2015: MR, US, Histo, X-Ray. d Multi-modalities of ImageCLEF 2016 [120]: MR, US, Histo, X-Ray, CT, PET, Endo, Dermo, EEG, ECG, EMG, Microscopy, Fundus (Electron Microscopy). e Multi-modalities of AREN0534: CT, MR, PET, US. f Multi-modalities of ImageCLEF 2016: MR, US, Histo, X-Ray, CT, PET, Endo, Dermo, Others, EEG, ECG, EMG, Electron Microscopy, Fundus Photography. g Multi-modalities of CMB-CRC: CT, MR, US, DX, PET, WSI. h Multi-modalities of CMB-MEL: CT, US, WSI, PET (SWI). i Multi-modalities of AREN0532: CT, MR, US, PET. Abbreviations: Seg=Segmentation, Cls=Classification, Est=Estimation, Recon=Reconstruction, Meas=Measurement, Track=Tracking; US=Ultrasound, Histo=Histopathology, WSI=Whole-slide image, Endo=Endoscopy, Dermo=Dermoscopy. 134 Table 11: 2D X-Ray datasets. #DatasetYearDimModalityStructureImagesLabelTaskDiseases 94Chest X-ray [167]20182DX-RayLung5.9kYesClsPneumonia 95CoronaHack [168]20202DX-RayLung5.9kYesClsCOVID-19, Pneumonia 96NIH Chest X-ray 14 [169]20172DX-RayLung112.1kYesClsThorax diseases 97COVIDx CXR-2 [170]20202DX-RayLung30.9kYesClsCOVID-19 98Pneumothorax Masks X-Ray [171]20202DX-RayLung12.0kYesSegPneumothorax 99IRMA X-ray [172]20202DX-RayBrain, Lung14.7kYesClsNA 100Chest XR COVID-19 [173]20212DX-RayLung21.4kYesClsCOVID-19 101COVID-19-Image [174]20202DX-RayLung93YesClsCOVID-19 102Chest X-ray PA Dataset [175]20212DX-RayLung4.6kNoClsCOVID-19, Pneumonia 103NHANES I X-ray [176]20212DX-RayLung17.1kNoNANA 104KNOAP2020 [140]20202D, 3DMR, X-RayKnee30YesPredOsteoarthritis 105AASCE [177]20192DX-RaySpine609YesRegNA 106Covid-19 Image Dataset [178]20212DX-RayLung345YesClsLung diseases 107 Pulmonary Chest X-Ray (ChinaSet) [179] 20212DX-RayLung800YesClsLung diseases 108MURA [180]20212DX-RayMulti-bone a 40.0kYesClsMusculoskeletal 109SIIM-ACR Pneumothorax Seg [171]20202DX-RayLung12.1kYesSegPneumothorax 110MIAS Mammography [181]20212DX-RayBreast322YesClsBreast cancer 111MedMNIST [118]20202D Multi b Retina, Breast, Lung100kYesClsMulti-diseases 112RSNA Pneumonia Detection [182]20182DX-RayLung26.7kYesDetLung diseases 113VinBigData Chest X-ray [183]20202DX-RayLung15.0kYesDetHeart atrium 114CheXpert [64]20212DX-RayLung224.3kYesClsDiabetic retinopathy 115 SIIM-FISABIO-RSNA COVID-19 [184] 20212DX-RayLung6.1kYesDetTuberculosis 116NODE21 [185]20212DX-RayLung5.5kYesDetBreast cancer 117ImageCLEF 2016 [120]20162DMulti c Skin, Cell, Breast31.0kYesClsHead & Neck tumor 118TCB-Challenge [186]20162DX-RayBone174YesClsOsteoporotic bone 119CRASS [187]20122DX-RayClavicle518YesSegClavicles 120COVIDGR [188]20202DX-RayLung852YesClsCOVID-19 121ChestX-Det [189]20212DX-RayLung3.6kYesSegLung diseases 122RANZCR CLiP [190]20202DX-RayBreast30.1kYesClsNA 123CPCXR [191]20202DX-RayLung1.2kYesNAPneumonia, COVID-19 124JSRT [192]20002DX-RayLung247YesClsLung nodule 125Synthetic COVID-19 CXR [193]20202DX-RayLung21.3kYesCls, GenCOVID-19 126Cephalometric X-ray Image [194]20142DX-RaySkull400YesLocNA 127CMB-CRC [145]20222D, 3D Multi d Colon472NoSeg, ClsColorectal cancer 128MIDRC-RICORD-1c [195]20212DX-RayLung1.3kYesClsNA 129Chest X-ray Imaging [196]20172DX-RayLung5.9kYesClsNA 130COVID-19 Chest X-ray DB [197]20212DX-RayNA3.9kYesClsCOVID-19 131SZ-CXR [198]20182DX-RayLung566YesSegNA 132Pulmonary Chest X-Ray Seg [179]20212DX-RayLung800YesSegLung diseases 133DENTEX [199]20232DX-RayBrain1.0kYesDetNA 134CL-Detection2023 [200]20232DX-RayNA555YesDetNA 135ISBI2023 CEPHA29 [201]NA2DX-RayNA1.0kYesLocNA 136ARCADE [202]20232DX-RayNA1.5kYesSegNA 137MedFM2023 [203]20232DX-RayNA4.8kYesClsNA 138CoronARe [204]NA2DX-RayNA0YesReconCoronary artery diseases 139VICTRE [205]20192DX-RayBreast217.9kNoNANA 140APOLLO-5 [149]20222D, 3DMulti e NA6.2kNoNANA 141CMB-LCA [146]20222D, 3D Multi f NA0NoNANA 142AHOD0831 [135]20222D, 3DMulti g NA0NoNAHodgkin Lymphoma 143CheXmask [206]20232DX-RayNA676.8kYesSegLung diseases 144Knee Osteoarthritis Dataset [207]20202DX-RayKnee0YesClsKnee osteoarthritis 145RUS_CHN [208]20212DX-RayHand0YesClsHand joints 146RSNA Bone Age [209]20172DX-RayHand14.2kYesEstHand bone 147CXR-LT [210]20232DX-RayBreast, Lung377.1kYesClsMulti-diseases 148PENGWIN2024-Task2 [211, 212]20252DX-RayPelvic Bone150YesSegPelvic bone fragments 149ICG-CXR [213]20252DX-RayLung11.4kYesGenLung diseases Overall2014∼20252DMultiFull Body2.1mNAMultiMulti a Structures of MURA: Elbow, Finger, Forearm, Hand, Humerus, Shoulder, Wrist. b Multi-modalities of MedMNIST: OCT, X-Ray, CT, Pathology, Fundus. c Multi-modalities of ImageCLEF 2016: MR, US, Histo, X-Ray, CT, PET, Endo, Derm, EEG, ECG, EMG, Microscopy, Fundus. d Multi-modalities of CMB-CRC: CT, MR, US, DX, PET, WSI. e Multi-modalities of APOLLO-5: CT, MR, US, PET, X-Ray. f Multi-modalities of CMB-LCA: CT, MR, US, Histo, DX. g Multi-modalities of AHOD0831: CT, MR, PET, X-Ray. Abbreviations: Seg=Segmentation, Det=Detection, Cls=Classification, Recon=Reconstruction, Reg=Registration, Loc=Localization, Est=Estimation, Pred=Prediction, Gen=Generation. 135 Table 12: 2D OCT datasets. #DatasetYearDimModalityStructureImagesLabelTaskDiseases 150OCT2017 [196]20182DOCTRetina83.5kYesClsNA 151Retinal OCT - C8 [41]20212DOCTRetina24kYesClsNA 152iChallenge - AGE19 [214]20192DOCTRetina1.6kYesClsNA 153DRAC22 [215]20222DOCTRetina174YesSeg Diabetic Retinopathy Lesions 154iChallenge - GOALS [216]20222DOCTRetina300YesSegNA 155Eye OCT Datasets [217, 218]20212DOCTRetina148YesClsNA 156APTOS-2021 [219]20222DOCTRetina2.6kYesPredDiabetic Retinopathy 157APTOS Cross-Country Datasets_stage1 [219]20222DOCTRetina2.6kYesPredNA 158MedMNIST [118]20202DMulti a Retina, Breast, Lung100kYesClsNA 159Canada OCT Retinal Images (Subset) [220]20182DOCTRetina25YesSegRetinal Structures 160SinaFarsiu-002-Fang_TMI_2013 [221]20132DOCTRetina195YesSegNA 161SinaFarsiu-003-Fang_BOE_2012 [221]20122DOCTRetina51YesSegNA 162SinaFarsiu-008-Chiu_BOE_2012 [221]20122DOCTRetina23YesSegNA 163SinaFarsiu-009-Chiu_BOE_2013 [221]20132DOCTRetina840YesSegNA 164SinaFarsiu-010-Rabbani_IOVS_2014 [222]20152DOCTRetina24YesSegNA 165SinaFarsiu-012-Estrada_TMI_2015 [223]20152DOCTRetina60YesSegNA 166SinaFarsiu-013-Estrada_PAMI_2015 [224]20152DOCTRetina90YesSegNA 167SinaFarsiu-018-Yang_BOE_2021 [225]20212DOCTRetina784YesSegNA 168APTOS Cross-Country Datasets_stage2 [219]20222DOCTRetina3.3kYesPredDiabetic Retinopathy 169OCTA-500_2D-Fundus [226]20202DOCTRetina500YesSegN/A 170OCTA2024 (MuTri)_2D-Fundus [227]20242DOCTRetina848YesSegNA Overall2012∼20222DMultiRetina, Breast, Lung221.7kYesMultiMulti a Multi-modalities of MedMNIST: OCT, X-Ray, CT, Pathology, Fundus Photography. Abbreviations: Seg=Segmentation, Cls=Classification, Pred=Prediction, Det=Detection, Recon=Reconstruction, Reg=Registration, Loc=Localization, Est=Estimation. 136 Table 13: 2D fundus datasets. #DatasetYearDimModalityStructureImagesLabelTaskDiseases 171DRISHTI-GS [228]20142DFundus PhotoRetina101YesSegOptic Disc 172CHASE [229]20092DFundus PhotoRetina28YesSegNA 173STARE [230]20042DFundus PhotoRetina40YesSegNA 174DRIVE [231]20032DFundus PhotoRetina40YesSegNA 175IDRID2018 [232]20182DFundus PhotoRetina81YesSeg, ClsDiabetic Retinopathy 176EyePACS [233]20152DFundus PhotoRetina88.7kYesClsDiabetic Retinopathy 177DRHAGIS [234]20172DFundus PhotoRetina40YesSegDR Lesions 178ODIR [235]20192DFundus PhotoRetina8kYesCls Ocular Diseases (DR screening) 179RIADD (RFMiD) [236]20202DFundus PhotoRetina3.2kYesClsRetinal Diseases 180MESSIDOR-2 [237]20132DFundus PhotoRetina1.7kYesClsDiabetic Retinopathy 181iChallenge-ADAM [238]20202DFundus PhotoRetina400YesClsDiabetic Retinopathy 182AIROGS [239]20212DFundus PhotoRetina101.4kNoClsDiabetic Retinopathy 183DiaRetDB [240]20092DFundus PhotoRetina89NoDetDR Lesions 184HRF [241]NA2DFundusRetina45NoSegNA 185iChallenge-PALM19 [242]20192DFundusRetina800YesSegNA 186Retina Fundus Image Reg. [243]20212DFundus PhotoRetina129YesRegNA 187APTOS-2019 [244]20212DFundus PhotoRetina3.7kYesClsDiabetic Retinopathy 188MedMNIST [41]20202DMulti a Retina, Breast, Lung100kYesClsNA 189DeepDR-Task1 [245]20202DFundus PhotoEye Vessel2kYesClsBreast Cancer 190ImageCLEF 2016 [246]20152DMulti b Skin, Cell, Breast31kYesClsHead & Neck Tumor 191RITE [247]20132DFundusRetina40YesSegRetinal Vessel 192GAMMA (Task1, CFP) [248]20212DFundus (CFP)Retina200YesClsGrading 193RIM-ONE [249]20202DFundusRetina485YesSegOptic Disc and Cup 194APTOS 2019 Blindness Det. [219]20192DFundusRetina5.6kYesClsGrading 195Glaucoma Detection [250]20202DFundusRetina650YesClsGlaucoma 196ACRIMA [251]20192DFundusRetina705YesClsGlaucoma 197AO-SLO Photoreceptor Seg. [252]20132DFundusRetina840YesSeg AO-SLO Cone Photoreceptor 198Arteriovenous Nicking [253]NA2DFundusRetina90YesCls Retinal Artery-Vein Nicking 199Retina [254]20192DFundusRetina601YesClsFundus Diseases 200Yangxi [255]20192DFundusRetina20.4kYesClsEye Axis 201William Hoyt [256]20042DFundusRetina856YesClsFundus Diseases 202Vampire [256]20112DFundusRetina8YesSegVessel 203 Retinal Fundus Imgs for Glaucoma [257] 20182DFundusRetina2.9kYesClsNA 204RetinaCheck (IOSTAR) [258]20162DFundusRetina30YesSegVessel 205Ophthalmic Slit Lamp [259]20182DFundusRetina60NoNANA 206Miles Iris [254]20132DFundus (Iris)Retina833NoClsRetinal Structures 207JSIEC [260]20192DFundusRetina1kYesClsFundus Diseases 208INSPIRE (Stereo) [254]20112DFundusRetina30YesRegNA 209INSPIRE (AVR) [254]20112DFundusRetina40YesRegNA 210HRF Quality Assessment [261]20132DFundusRetina36YesRegNA 211HRF Segmentation [241]20132DFundusRetina45YesSegVessel 212iChallenge-REFUGE2 [262]20202DFundus Photo (CFP)Retina1.6kYesClsGlaucoma 213GAMMA [248]20212D, 3DFundusRetina200YesClsNA 214OIA-ODIR [235]20192DFundusNA10kYesClsNA 215VARPA [263]20192DFundusRetina58YesClsNA 216ORVS [264]20202DFundusRetina49YesSegNA 217Retinal Img Quality Assess [265]20202DFundusRetina216YesClsNA 218iChallenge-GAMMA_3D-OCT [248]20212DFundusRetina300YesSegGlaucoma 219DeepDR-Task2 [245]20202DFundusNA2kYesRegNA 220DeepDR-Task3 [245]20202DFundusNA246YesClsNA 221MMAC2023 [266]20232DFundusNA0YesClsNA 222RFMiD 2.0 [236]20232DFundus PhotoNA860YesCls Retinal Fundus Multi-Disease 223MuReD [267]20222DFundus PhotoNA2.2kYesClsRetinal Diseases 224Retinal Vessel Tortuosity20082DFundus PhotoRetina60YesRegNA 225ImageCLEF 2016NA2DMulti c Skin, Cell, Breast31kYesClsNA 226PARAGUAY [268]NA2DFundus PhotoNA0YesClsDiabetic Retinopathy 227BEH [269]NA2DFundus PhotoNA0YesNAGlaucoma 228BiDRNA2DFundus PhotoNA0YesNADiabetic Retinopathy 229HarvardGlaucomaNA2DFundus PhotoNA0YesNAGlaucoma 230FUNDNA2DFundus PhotoNA0YesNANA 231LAG [270]NA2DFundus PhotoNA0YesNAGlaucoma 232DHRFNA2DFundus PhotoRetina6.2kYesClsDiabetic Retinopathy 233E-ophtaNA2DFundus PhotoRetina926YesSegNA 234FIVES [271]NA2DFundus PhotoRetina800YesSegVessel 235OcularD [272]NA2DFundus PhotoRetina6.4kYesClsNA 236PAPILA [273]NA2DFundus PhotoRetina488YesSegNA 237Papilledema [274]20182DFundus PhotoRetina1.4kYesClsPapilledema 238ROD20232DFundus PhotoRetina281YesClsRetinal Occlusion 239ToxoFundus [275]20232DFundus PhotoRetina411YesClsOcular Toxoplasmosis 240GAMMA (Task3, CFP) [248]20212DFundus (CFP)Retina200YesSegOptic Disc and Cup 241 iChallenge-GAMMA_2D- Fundus [248] 20212DFundusRetina300YesSegGlaucoma Overall2003∼20232DMultiMulti443.1kNAMultiMulti a Multi-modalities of MedMNIST: OCT, X-Ray, CT, Pathology, Fundus Photography. b Multi-modalities of ImageCLEF 2016: MR, US, Histopathology, X-Ray, CT, PET, Endoscopy, Dermoscopy, EEG, ECG, EMG, Electron Microscopy, Fundus Photography. c Multi-modalities of ImageCLEF 2016: MR, US, Histopathology, X-Ray, CT, PET, Endoscopy, Dermoscopy, EEG, ECG, EMG, Microscopy, Fundus Photography. Abbreviations: Seg=Segmentation, Det=Detection, Cls=Classification, Reg=Registration, US=Ultrasound, DR=Diabetic Retinopathy. 137 Table 14: 2D dermoscopy datasets. #DatasetYearDimModalityStructureImagesLabelTaskDiseases 242ISIC18 [276]20182DDermoscopySkin2.7kYesSegSkin lesion 243ISIC20 [277]20202DDermoscopySkin33.1kYesCls Benign melanoma, malignant melanoma 244ISIC16 [278]20162DDermoscopySkin1.3kYesSegSkin lesion 245ISIC17 [279]20162DDermoscopySkin2.8kYesSegSkin lesion 246Derm7pt [280]20212DDermoscopySkin2.0kYesClsSkin lesion 247ISIC19 [25]20192DDermoscopySkin25.3kYesClsCells 248Fizpatrick 17k [281]20212DDermoscopySkin16.6kYesClsNA 249MED-NODE [282]20152DDermoscopySkin170YesClsBrain 250PAD-UFES-20 [283]20202DDermoscopySkin2.3kYesClsThoracic diseases 251PH2 [284]20142DDermoscopySkin200YesClsCells 252DFUC 2020 [285]20202DDermoscopyFoot2.0kYesSegBreast cancer 253SD-128 / SD-198 / SD-260 [286, 287]20212DDermoscopySkin6.6kYesClsFetal structure 254ImageCLEF 2016 [144]20152DMulti a Skin, Cell, Breast31kYesClsHead & neck tumor 255Monkeypox Skin Image Dataset [288, 289]20222DDermoscopySkin40.2kYesClsMonkeypox 256Vitiligo Images [290]20192DDermoscopySkin368NoNAVitiligo 257ImageCLEF 2016 [120]NA2DMulti a Skin, Cell, Breast31kYesClsNA Overall2014∼20222DMultiSkin, Cell, Breast197.6kNAMultiMulti a Multi-modalities of ImageCLEF 2016: MR, US, Histopathology, X-Ray, CT, PET, Endoscopy, Dermoscopy, EEG, ECG, EMG, Microscopy, Fundus Photography. Abbreviations: Seg=Segmentation, Det=Detection, Cls=Classification, Recon=Reconstruction, Reg=Registration, Loc=Localization, Est=Estimation, US=Ultrasound, EM=Electron Microscopy. 138 Table 15: 2D histopathology datasets. (part 1/2) #DatasetYearDimModalityStructureImagesLabelTaskDiseases 258PANDA_radboud [291]20202DHistopathology (Patch)Prostate5.1kYesSegProstate Cancer 259Gleason [292]20192DHistopathology (Patch)Prostate331YesSegProstate Cancer 260PathologyVQA [293]20202DHistopathology (Patch)Full Body5.0kYesVQANA 261SLN-Breast [294]20192DHistopathology (WSI)Lymph166YesClsBreast Lymph Node 262MoNuSeg [295]20182DHistopathology (Patch)Nuclei51YesSegNA 263MoNuSAC2020 [296]20192DHistopathology (Patch)Lung, Prostate, etc. a 914YesSegNA 264DigestPath19 [297]20192DHistopathology (WSI)Colon212YesDetSignet Ring Cell 265CAMELYON17 [298]20162DHistopathology (WSI)Breast500YesClsBreast Cancer 266ANHIR [299]20182DHistopathology (WSI) Kidney, Breast, etc. b 481YesRegNA 267Overlapping Cervical Cells [300]20152DHistopathology (Patch)Cervix17YesSegCervical Cells 268MIDOG2022 [301]20222DHistopathology (Patch)Lung, Breast, Skin405YesDetMitotic Figure 269ACROBAT [302]20232DHistopathology (WSI)Breast750YesRegNA 270BRIGHT [303]20212DHistopathology (Patch)Breast5.1kYesClsPathological Benign 271CoNIC2022 [304]20222DHistopathology (Patch)Colon5.0kYesSegColon Nuclei 272PanNuke [305]20212DHistopathology (WSI)Multi-organ481YesSeg, ClsMultiple Cancers 273Malignant Lymphoma Cls [306]20212DHistopathology (Patch)Lymph374YesClsLymphoma 274PAIP2021 [307]20212DHistopathology (WSI)Colon, Prostate150YesDetColon/Prostate Cancer 275Breast Cancer Cell Seg [308]20212DHistopathology (Patch)Breast58YesSegBreast Cancer 276MedMNIST [309]20202DMulti c Retina, Breast, Lung100kYesClsMulti-disease 277Histopathologic Cancer Det [310]20182DHistopathology (Patch)Lymph220kYesClsBreast Cancer 278HuBMAP [311]20202DHistopathology (Patch)Kidney15YesSegKidney Tissue 279ACDC-LungHP [312]20192DHistopathology (WSI)Lung200YesSegLung Cancer 280SegPC 2021 [313]20212DHistopathology (Patch)Blood498YesSegPlasma Cells 281MIDOG2021 [301]20212DHistopathology (Patch)Full Body200YesDetProstate Cancer 282Dermofit Image Library [314]20212DHistopathology (Patch)Skin1.3kYesClsLung Adenocarcinoma 283Weakly Supervised Cell Seg [315]20222DHistopathology (Patch)Full Body30YesSegProstate Cancer 284TIGER-wsibulk [316]20222DHistopathology (WSI)Breast93YesSegPneumothorax 285BCI [317]20222DHistopathology (Patch)Breast4.9kYesGenLesion 286WSSS4LUAD [318]20212DHistopathology (Patch)Lung10.2kYesSegCoronary Artery 287Breast Cancer Seg [319]20192DHistopathology (Patch)Breast151YesSegNeurons 288NuCLS [320]20212DHistopathology (Patch)Nuclei3.1kYesSegKidney 289ImageCLEF 2016 [144]20152D Multi d Skin, Cell, Breast31kYesClsHead & Neck Tumor 290PAIP2020 [321]20202DHistopathology (WSI)Liver118YesClsColorectal Cancer 291HEROHE [322]20192DHistopathology (WSI)Lung510YesClsGI diseases 292Lymphocyte Assessment [323]20192DHistopathology (Patch)Lymphocyte20kYesClsLymphocyte Number 293LYON19 [324]20192DHistopathology (Patch)Lymphocyte441YesClsLymphocytes 294GlaS [325]20152DHistopathology (Patch)Cell165YesSeg Colorectal Adenocarcinoma 295CoNSeP [326]20182DHistopathology (Patch)Colon41YesSegColorectal Nuclei 296PCam [40]20182DHistopathology (Patch)Breast328kYesSegMetastatic Tissue 297LC25000 [38]20192DHistopathology (Patch)Colon25kYesClsLung and Colon Tissue 298PanNuke (Seg) [305]20212DHistopathology (Patch)Full Body7.9kYesSegNucleus 299BreakHis (40x) [39]20162DHistopathology (Patch)Breast2.0kYesClsBreast Tumors 300SICAPv2 [327]20202DHistopathology (Patch)Prostate18.8kYesClsProstate Cancer 301Kumar [295]20182DHistopathology (Patch)Cell54YesSegMulti-organ Nuclei 302HErlev [328]20082DHistopathology (Patch)Cervix5.6kYesClsCervical Cancer 303CRC100K [329]20182DHistopathology (Patch)Colon100kYesClsColorectal Cancer 304BRCA-M2C [330]20212DHistopathology (Patch)Breast120YesSegBreast Cancer 305warwick [325]20152DHistopathology (Patch)Colon330YesSegColorectal Gland 306CRAG [331]20192DHistopathology (Patch)Colon213YesSegColorectal Cancer 307Chaoyang [332]20212DHistopathology (Patch)Blood6.2kYesClsRed Blood Cell 308CMB-CRC [122]20223D, 2DMulti e Colon472NoSeg, ClsColorectal Cancer 309CMB-GEC [123]20223D, 2D CT, Histopathology (WSI), PET Brain14NoSeg, ClsMelanoma 310CMB-MEL [124]20223D, 2D Multi f Brain255NoSegMelanoma 311CMB-MML [125]20212D, 3DMulti g NA60NoPredMultiple Myeloma 312CMB-PCA [126]20222D, 3D CT, MR, Histopathology (WSI) Prostate31NoCls, PredProstate Cancer 313AGGC22 [333]20222DHistopathology (Patch)Gland150YesSegGland Segmentation 314TUPAC [334]20152DHistopathology (Patch)Brain573YesRegBreast Cancer 315Prostate Fused-MRI-Pathology [147]20162D, 3D MR, Histopathology (WSI) Prostate29NoNAProstate Cancer 316Malaria Cell Image Dataset [335]20212DHistopathology (Patch)Cell27.6kYesClsMalaria 317HEp-2 Cell Classification [336]20202DHistopathology (Patch)Cell13.6kYesClsHEp-2 Cells 318Breast Cancer Cell Seg Dataset [337]20202DHistopathology (Patch)Breast, Cell58YesSegBreast Cancer a Full structure of MoNuSAC2020: Lung (Thorax), Prostate (Pelvis), Kidney (Abdomen), Breast (Thorax). b Full structure of ANHIR: Kidney (Abdomen), Breast (Thorax), Colon (Abdomen), Spleen, Lung (Thorax). c Multi-modalities of MedMNIST: OCT, X-Ray, CT, Histopathology (Patch), Fundus Photography. d Multi-modalities of ImageCLEF 2016: MR, US, Histopathology (Patch), X-Ray, CT, PET, Endoscopy, Dermoscopy, Others. e Multi-modalities of CMB-CRC: CT, MR, US, DX, PET, Histopathology (WSI). f Multi-modalities of CMB-MEL: CT, US, Histopathology (WSI), PET. g Multi-modalities of CMB-MML: CT, MR, PET, Histopathology (WSI). Abbreviations: Seg=Segmentation, Det=Detection, Cls=Classification, Reg=Registration, VQA=Visual Question Answering, Gen=Generation, Pred=Prediction. 139 Table 16: 2D histopathology datasets. (part 2/2) #DatasetYearDimModalityStructureImagesLabelTaskDiseases 319TIGER-wsirois [338]20222DHistopathology (Patch)Breast2.0kYesSegBreast Cancer 320TIGER-wsitils[338]20222DHistopathology (Patch)Breast82YesRegBreast Cancer 321Breast Cancer Cell Seg 2 [337]20202DHistopathology (Patch)Breast58YesSegBreast cancer 322 Malignant Lymphoma Cls Dataset [306] 20202DHistopathology (Patch)Lymph374YesClsLymphoma 323Lung and Colon Histopathology [38]20202DHistopathology (Patch)Lung, Colon25kYesCls Lung and Colon Cancer 324FocusPath [339]20202DHistopathology (Patch)NA864YesIQAHistopathology Image 325Blood Cell Images [340]20192DHistopathology (Patch)Blood12.5kYesDetBlood Cell 326Colorectal Histology MNIST [341]20162DHistopathology (Patch)Colon5.0kYesClsColorectal Tissue 327BreakHis 100x [39]20162DHistopathology (Patch)Breast9.1kYesClsBreast Cancer 328BreakHis 200x [39]20162DHistopathology (Patch)Breast9.1kYesClsBreast Cancer 329BreakHis 400x [39]20162DHistopathology (Patch)Breast9.1kYesClsBreast Cancer 330BCNB Task-1 [342]20212DHistopathology (WSI)Breast1.1kYesClsLeukemia 331BCNB Task-2 [342]20212DHistopathology (WSI)Breast1.1kYesClsBreast Cancer 332BCNB Task-3 [342]20212DHistopathology (WSI)Breast1.1kYesClsBreast Cancer 333BCNB Task-4 [342]20212DHistopathology (WSI)Breast1.1kYesClsBreast Cancer 334BCNB Task-5 [342]20212DHistopathology (WSI)Breast1.1kYesClsBreast Cancer 335BCNB Task-6 [342]20212DHistopathology (WSI)Breast1.1kYesClsBreast Cancer 336PANDA [291]20202DHistopathology (Patch)Prostate10.6kYesClsProstate Cancer 337PANDA_karolinska [291]20202DHistopathology (Patch)Prostate5.5kYesSegProstate Cancer 338PAIP 2023 [343]20222DHistopathology (Patch)Pancreas103YesSegLiver Cancer 339ATEC23 [344]20232DHistopathology (WSI)Ovary468YesClsOvarian Cancer 340ACROBAT2023 [345]20232DHistopathology (WSI)Breast1.2kYesRegBreast Cancer 341OCELOT2023 [346]20232DHistopathology (WSI)Colon667YesDetColon Cancer 342OCEAN [347]20232DHistopathology (WSI)Ovary1.6kYesClsOvarian Cancer 343Endo-Aid [348]20222DHistopathology (WSI)GI Tract91NoClsGI Polyps 344PAIP2023 [343]20222DHistopathology (Patch)Pancreas103YesSegPancreatic Cancer 345PatchCamelyon[349]20182DHistopathology (Patch)Lymph Node295kYesClsMetastatic Tissue 346Bone Marrow Cytomorphology[350]20212DHistopathology (Patch)Bone Marrow171kYesClsBlood Cells 347Lung-Fused-CT-Pathology[132]20182D, 3D CT, Histopathology (WSI) Lung36YesSegLung Cancer 348HNSCC-mIF-mIHC[351]20202DHistopathology (Patch)Head & Neck3.2kNoNAHNSCC 349SN-AM[352]20192DHistopathology (Patch)Lymph Node190YesSegMelanoma 350Ovarian Bevacizumab Response[353]20232DHistopathology (WSI)Ovary285NoNAOvarian Cancer 351CMB-LCA[354]20222D, 3DMulti a Lung0NoNALung Cancer 352CPTAC-COAD[355]20212DHistopathology (WSI)Colon373YesCls Colon Adenocarcinoma 353 Hungarian-Colorectal- Screening[356] 20222DHistopathology (WSI)Colorectal200NoNAColorectal Polyps 354DLBCL-Morphology[357]20222DHistopathology (Patch)Lymph Node246YesSegDLBCL 355CPTAC-OV[358]20212DHistopathology (WSI)Ovary222NoNAOvarian Cancer 356CODEX imaging of HCC[359]20232DHistopathology (WSI)Liver646NoNALiver HCC 357Prostate-MRI[150]20113D, 2D Multi b Prostate26NoNAProstate Cancer 358CPTAC-BRCA[360]20212DHistopathology (WSI)Breast642NoNABreast Cancer 359AML-Cytomorphology_LMU[361]20192DHistopathology (WSI)Blood18.4kYesCls Acute Myeloid Leukemia 360MiMM_SBILab [362]20192DHistopathology (WSI)Bone Marrow85YesLocMultiple Myeloma 361Pan-Cancer-Nuclei-Seg[363]20202DHistopathology (WSI)Multi-organ5.1kYesSegPan-Cancer 362TIL-WSI-TCGA[364]20182DHistopathology (WSI)Multi-organ5.2kYesClsPan-Cancer 363C-NMC 2019[365]20192DHistopathology (WSI)Blood15.1kYesClsLeukemia 364CPTAC-AML[366]20192DHistopathology (WSI)Bone Marrow122NoNA Acute Myeloid Leukemia 365CATCH[367]20222DHistopathology (WSI)Skin350YesSegSkin Cancer 366NADT-Prostate[368]20212DHistopathology (WSI)Prostate1.4kNoNAProstate Cancer 367HER2 tumor ROIs[369]20222DHistopathology (WSI)Breast273YesSegHER2+ Breast Cancer 368 CRC_FFPE- CODEX_CellNeighs[370] 20202DHistopathology (WSI)Colorectal200NoNAColorectal Cancer 369Post-NAT-BRCA [371]20192DHistopathology (WSI)Breast96YesClsBreast Cancer 370 Osteosarcoma Tumor Assessment[372] 20192DHistopathology (WSI)Bone1.1kYesClsOsteosarcoma 371Quilt-1M [373]20232DHistopathology (Patch)Multi-organ1mYesVQAMulti-organ Pathology Overall2008∼20232DMultiFull Body2.6mNAMultiMulti a Multi-modalities of CMB-LCA: CT, MR, US, Histopathology (WSI), DX. b Multi-modalities of Prostate-MRI: MR, CT, PET, Histopathology (WSI). Abbreviations: Seg=Segmentation, Det=Detection, Cls=Classification, Reg=Registration, Loc=Localization, IQA=Image Quality Assessment, VQA=Visual Question Answering. 140 Table 17: 2D microscopy datasets. #DatasetYearDimModalityStructureImagesLabelTaskDiseases 372CellTracking2019 [374]20192DMicroscopyCell1.4MYesTrackingNA 373CREMI [375]20162DMicroscopyBrain375YesSegNA 374Bacteria Detection [376]20212DMicroscopyNA366YesSegNA 375Blood Cell Images [340]20212DMicroscopyBlood12.5kYesClsBlood 376Leukemia Classification [377]20212DMicroscopyNA15.1kYesClsLeukemia 377CellTracking2021 [374]20212D+3D+VideoMicroscopyCell0YesTracking, SegLung Disease 378B-ALL Classification [378]20182DMicroscopyCell15.1kYesClsBrain Tumor 3792018 Data Science Bowl [379]20182DMicroscopyNuclei670YesSegSkin Lesions 380GSB2016 [157]20152DMulti a Skin, Cell, Breast31kYesClsHead & Neck Tumor 381OCCISC (SemSeg) [300]20142DMicroscopyCell945YesSegCervical Cytology 382ICIAR 2018 (Microscopy) [380]20172DMicroscopyBreast400YesClsBreast Cancer 383CBC (Counting) [381]20192DMicroscopyFull Body420YesRegNA 384HuSHeM [382]20172DMicroscopyPelvic216YesCls Sperm Head Morphology 385Kaggle-HPA [383]20212DMicroscopyNA89.5kYesSegProtein Localization 386nanni2016texture [384]20162DMicroscopyRetina195YesClsCell Shape 387Corneal Endothelial Cell [385]20192DMicroscopyRetina385YesSegNA 388Corneal Nerve [386]20082DMicroscopyRetina90YesClsCorneal Abnormalities 389Corneal Nerve Tortuosity [387]20112DMicroscopyRetina30YesClsNerve Tortuosity 390Cervix93 Cytology [388]20182DMicroscopyCervix93YesClsCervical Cancer 391DLBCL-Morph [389]20202DMicroscopyRetina152.2kYesRegDLBCL Lymphoma 3922-PM Vessel Dataset [390]20162DMicroscopyVessel12YesSegNA 393BBBC041 [391]20122DMicroscopyCell1.3kYesSegMalaria 394FMD [392]20192DMicroscopySurface5.1kYesCls, SegSurface Defect 395Blood Cell Detection [393]20222DMicroscopyNA874YesDetNA 396Tuberculosis Image [394]20202DMicroscopyNA1.3kYesDetTuberculosis 397MHSMA [395]20192DMicroscopyNA1.5kYesClsNA 398ICIAR 2018 (Microscopy) [380]20172DMicroscopy, WSINA400YesSegBreast Cancer 399ImageCLEF 2016 [157]20162DMulti a Skin, Cell, Breast31kYesClsNA 400CellTracking2024 [374]20242D+3D+VideoMicroscopyCell0YesTracking, SegNA 401CellTracking2022 [374]20222D+3D+VideoMicroscopyCell0YesTracking, SegNA 402CellTracking2023 [374]20232D+3D+VideoMicroscopyCell0YesTracking, SegNA 403OCCISC (InstSeg) [381]20142DMicroscopyCell945YesSegNA 404CBC (Detection) [394]20192DMicroscopyFull Body420YesDetNA 405ICIAR 2018 (WSI) [380]20182DMicroscopy, WSINA400YesSegBreast Cancer Overall2008∼20242DMultiFull Body1.8mYesMultiMulti a Multi-modalities of GSB2016 and ImageCLEF 2016: MR, US, Histopathology, X-Ray, CT, PET, Endoscopy, Dermoscopy, EEG, ECG, EMG, Microscopy, Electron Microscopy, Fundus Photography. Abbreviations: Seg=Segmentation, Det=Detection, Cls=Classification, Reg=Registration, Tracking=Tracking, WSI=Whole-Slide Images. Table 18: 2D infrared datasets. #DatasetYearDimModalityStructureImagesLabelTaskDiseases 406RAVIR [396]20222DInfraredRetina42YesSegBlood vessel 407MRL Eye Glasses cls [397]20182DInfraredRetina84.9kYesClsNA 408MRL Eye Eye state cls [397]20182DInfraredRetina84.9kYesClsNA 409MRL Eye Reflections cls [397]20182DInfraredRetina84.9kYesClsNA 410MRL Eye Image quality cls [397]20182DInfraredRetina84.9kYesClsNA 411MRL Eye Sensor type cls [397]20182DInfraredRetina84.9kYesClsNA Overall2018∼20222DInfraredRetina424.5kYesCls, SegBlood vessel Abbreviations: Seg=Segmentation, Cls=Classification. 141 Table 19: 2D endoscopy datasets. #DatasetYearDimModalityStructureImagesLabelTaskDiseases 412Kavsir[398]20172DEndoscopyColon14kYesClsNA 413EndoSlam[399]20212DEndoscopy Colon, Liver, Stomach, Kidney 76.8kYesRecon, EstNA 414SARAS-MESAD[88]20212DEndoscopyProstate50.3kNoDetGI disease 415EAD19[87]20182DEndoscopyStomach, Bladder, Colon2.1kYesDetEndo Artifact 416EndoCV2020-Sub Challenge1[400]20192DEndoscopyColon2.3kYesDet, SegPolyp 417EndoVis15[401]20152DEndoscopyColon612YesSegPolyp 418 Surgical tool detection challenge (m2cai16-tool)[402] 20162DEndoscopyGallbladder15YesDetNA 419AIDA-E_1 [403]20152DEndoscopyStomach, Liver181YesClsCeliac Disease 420AIDA-E_2 [404]20152DEndoscopyEsophagus157YesClsBarrett’s Esophagus 421AIDA-E_3 [405]20152DEndoscopyStomach, Colon88YesClsMetaplasia, Dysplasia 422CVC-ClinicDB[406]20212DEndoscopyBowel1.4kYesSegPolyp 423Kvasir-SEG[407]20202DEndoscopyBowel8kYesSegNA 424FetReg[408]20222DEndoscopyUterus2.7kYesSegPlacental Vasculature 425SARAS-ESAD[88]20202DEndoscopyBowel33.4kYesDetSkin lesion 426ImageCLEF 2016[120]20152DMulti a Skin, Cell, Breast31kYesClsH&N tumor 427ISBI-AIDA-CECI20152DEndoscopyLiver, Stomach181YesClsCeliac diseases 428SUN_SEG[409]20222D+VideoEndoscopyColon49.1kYesSeg, Det, ClsPolyp 429HyperKvasir[83]20202D+VideoEndoscopy Esophagus, Stomach, Colon 6.5kYesCls,Caption, Loc GI disease 430 Gastrointestinal Image ANAlysis (GIANA) [410] 20162DEndoscopyColon600YesClsVascular Malformation 431EndoVis 2015 - DAGI [411]20152DEndoscopyNA389YesDetCholecystectomy 432EndoVis 2015 - EBCD [412]20152DEndoscopyNA150YesSegBarrett’s Epithelium 433EndoCV2020-Sub Challenge2[413]20192DEndoscopyNA386YesDetNA 434EndoVis 2015 - APDCV[401]20152DEndoscopyNA612YesSegColonic Polyp 435EndoVis 2015 - IST_2D-Endoscopy [414]20152D+VideoEndoscopyNA100YesSegSurgical Instruments 436EndoVis 2018 - RSS[415]20182DEndoscopyNA2.8kYesSegSurgical Instruments 437ISBI-AIDA-EMIBS20152DEndoscopyNA262YesClsGastric 438ISBI-AIDA-GCICS20152DEndoscopyNA176YesClsGastric 439EndoVis2023-SIMS [416]20232DEndoscopyNA0YesSegEndoscopy 440EndoVis2023-Syn-ISS [417]20232DEndoscopyNA0YesSegNA 441P2ILF [418]20222D+3DEndoscopyNA15YesRegMulti-organ 442EndoVis2023-SurgRIPE[419]20232DEndoscopyNA0YesEstNA 443m2caiSeg[420]20202DEndoscopyInstrument614YesSegNA 444CVC-EndoSceneStill[406]NA2DEndoscopyNA3.4kYesSegPolyp 445Endo-FM[421]NA2D+VideoEndoscopyNA0YesSeg, Cls, DetNA 446SegSTRONG-C[422]NA2D+VideoEndoscopyNA17YesSegNA 447SegCol[423]NA2D+Video Colposcopy, Endoscopy NA78YesSegNA 448FedSurg [424]20242D+VideoEndoscopyNA30YesCls Laparoscopic appendectomy Overall2015∼20242DMultiFull Body288.5kYesMultiMulti a Multi-modalities of ImageCLEF 2016: MR, Ultrasound, Histopathology, X-Ray, CT, PET, Endoscopy, Dermoscopy, Others, EEG, ECG, EMG, Electron Microscopy, Fundus Photography. Abbreviations: Seg=Segmentation, Det=Detection, Cls=Classification, Recon=Reconstruction, Reg=Registration, Loc=Localization, Est=Estimation, GI=Gastrointestinal, H&N=Head & Neck. Table 20: 2D datasets of the other modalities. #DatasetYearDimModalityStructureImagesLabelTaskDiseases 449 Intel & MobileODT Cervical Cancer Screening [425] 20172DColposcopyVagina1,993YesClsCervical cancer 450 ADDI ALZHEIMER’S DETECTION CHALLENGE [426] 20212DSeries DataNA34,614YesClsLung adenocarcinoma 451 The Digital Mammography DREAM Challenge [427] 20162DMammographyBreast640,000YesClsAtrophic AMD 452BigNeuron [428]20162DNABrain2,166YesReconDiabetic foot ulcer 453 Human Activity Classification with Radar [429] 20192DOthersNA1,854YesClsNA 454KvasirCapsule-SEG [430]20212D Colposcopy (Capsule) Polyp55YesSegPolyp 455SCDB [431]20202DOthersSkin4YesClsSkin lesion 456ROSE [432]NA2DOCTA (CT)Eye a 229YesSegNA 457CMMD [433]20212DMammographyBreast1,775YesSeg, ClsBreast Cancer 458Br35H [434]20222DMRBrain3,000YesClsBrain Tumor 459CDD-CESM [435]20212DMammographyNA2,006YesSeg, ClsBreast Cancer 460OralCancer [436]20202D Digital Photography Lip, Tongue131YesClsOral Cancer 461Oral_Diseases [437]20232D Digital Photography NA12,944YesCls, DetDental conditions 462MRL Eye Gender [438]20182D Infrared Reflectance Imaging Retina84,898YesClsNA 463MITOS-ATYPIA-14 [439]20132DBiopsy slidesBreast1,420YesClsBreast Cancer 464VinDr-Mammo [440]NA2DMammographyBreast19,992YesDetBreast Cancer 465VinDr-SpineXR [441]NA2DX-raySpine10,469YesNASpinal Lesions 466VinDr-PCXR [442]NA2DX-rayChest9,125YesNANA 467VinDr-CXR [443]NA2DX-rayChest18,000YesNANA 468HAM10000 [444]NA2DDermoscopySkin10,015YesClsSkin Cancer 469RFMiD [445]NA2DFundusRetina3,200YesClsOphthalmic Diseases 470Chaksu [446]NA2DFundusRetina1,345YesSegGlaucoma 471PBC_dataset_normal_DIB [447]NA2DMicroscopyBlood0YesClsNA Overall2013∼20232DMultiFull Body859.2kYesMultiMulti a Includes anatomical structures like optic nerve, eyeball, etc. Abbreviations: Seg=Segmentation, Det=Detection, Cls=Classification, Recon=Reconstruction. 142 Table 21: 3D CT datasets #DatasetYearDimModalityStructureVolumesLabelTaskDiseases 472TotalSegmentator[67]20223DCTFull Body1204YesSegVaried pathologies 473AutoPET[448]20223DCT/PETWhole-body1014YesSegCancer 474AutoPET I[449]20233DCT/ PETWhole-body1219YesSegCancer 475ULS23[450]20233DCT Chest, Ab- domen, Pelvis 39468YesSegOncological lesions 476DeepLesion[451]20182D/3DCT Bone, Ab- domen, Medi- astinum, Liver, Lung, Kidney... 32735YesDetVarious lesions 477CT-ORG[452]20193DCT Bladder, Bone, Brain, Kidney, Liver, Lung 140YesSeg Liver lesions, Metastatic disease 478SegTHOR[453]20193DCT Heart, Aorta, Trachea, Esophagus 60YesSegLung cancer 479AbdomenAtlas[454]20243DCTAbdomen20460YesSegMulti-organ 480CT-RATE[36]20243DCTChest50188YesClsChest abnormalities 481M3D[66]20243DCT/MRWhole-body120000YesSeg/Rec/Reg/LocMultiple pathologies 482AMOS22[70]20223DCT/MRAbdomen600YesSegMulti-disease 483AMOS-M[455]20243DCTAbdomen2300Yessyn/RecAbdominal diseases 484FLARE21[456]20213DCT Liver, Kid- ney, Spleen, Pancreas 511YesSegMulti-organ 485FLARE22[457]20223DCTAbdomen2300YesSegPan-cancer 486FLARE23[458]20233DCTAbdomen4500YesSegPan-cancer 487WORD[459]20213DCTAbdomen150YesSegCancer 488RAOS[460]20243DCT/MRAbdomen4130YesSegAbdominal cancers 489SLIVER07[461]20073DCTLiver30YesSegLiver 490LiTS[68]20173DCTLiver201YesSegLiver tumors 491Pancreas-CT[462]20163DCTPancreas80YesSegHealthy controls 492CHAOS CT[463]20193DCTLiver40YesSegHealthy subjects 493CHAOS MRI[463]20193DCT/MRAbdomen160YesSegHealthy 494KiTS19[464]20193DCTKidneys300YesSegKidney cancer 495KiTS21[465]20213DCTKidneys400YesSegKidney diseases 496KiTS23[466]20233DCTKidneys599YesSegKidney cancer 497KiPA22[467]20223DCTKidney130YesSegRenal Cancer 498AbdomenCT-1K[468]20213DCTAbdomen1062YesSegMulti-disease cases 499MSD03-Liver[469]20183DCTLiver210YesSegLiver cancer 500MSD06-Lung[469]20183DCTLung96YesSegLung cancer 501MSD07-Pancreas[469]20183DCTPancreas420YesSegPancreatic masses 502 MSD08- HepaticVessel[469] 20183DCTLiver443YesSegLiver tumors 503MSD09-Spleen[469]20183DCTSpleen61YesSeg 504MSD10-Colon[469]20183DCTColon190YesSegColorectal Cancer 505EXACT09[470]20093DCTLung40YesSegLung diseases 506LOLA11[471]20113DCTChest55YesSegLung abnormalities 507LUNA16[472]20163DCTLung888YesDet/ClsLung cancer 508ATM’22[473]20223DCTLung500YesSegPulmonary diseases 509AIIB23[474]20233DCTAirway312YesSegFibrotic lung disease 510 BIMCV COVID- 19+[475] 20202D/ 3DCT/ X-RAYLung2428YesSeg/ Cls/ LocCOVID-19 pneumonia 511COVID-19-AR[476]20202D/ 3DCT/ X-RAYChest105YesClsCOVID-19 512 CT Images in COVID- 19[477] 20203DCTChest771NoClsCOVID-19 pneumonia 513 Chest CT Scans with COVID-19[478] 20203DCTChest20YesClsCOVID-19 514 MIDRC-RICORD- 1a[479] 20203DCTChest120YesSeg/ClsCOVID-19 515 COVID-19-20 Lung CT Lesion Segmentation Challenge[480] 20203DCTLung295YesSegCOVID-19 516 COVID-19-NY- SBU[481] 20212D/ 3D CT/ MR/ PET/ X-RAY Brain/ Chest1384NoClsCOVID-19 517COVID19-CT-1000[482]20212D/ 3DCTLung1000YesClsCOVID-19 518 MIDRC-RICORD- 1B[483] 20213DCTChest120YesClsCOVID-19 negative 519STOIC2021[484]20213DCTLung10735YesClsCOVID-19 520 COVID-19 CT scans[485] 20213DCTLung20YesSegCOVID-19 infection 521COV19-CT-DB[486]20223DCTChest7750YesClsCOVID-19 522LNQ2023[487]20233DCTChest513YesSeg Lymph node metas- tases 523Parse2022[488]20223DCT Pulmonary artery 200YesSeg Pulmonary hyperten- sion 524LNDb[489]20193DCTLung294YesCls/Det/SegLung cancer 525FUMPE[490]20183DCTLung35YesSeg/DetPulmonary embolism 526InSTANCE2022[491]20223DCTBrain200YesSeg Intracranial Hemor- rhage 143 527ISLES 2024[492]20243DCT/MRBrain250YesSeg Acute/subacute ischemic stroke 528HaN-Seg[493]20233DCT/MRHead and Neck42YesSegHead and Neck Cancer 529SegRap2023[494]20233DCTHead, Neck400YesSeg Nasopharyngeal carcinoma 530PDDCA[495]20153DCT Brainstem, Mandible, Optic nerves, Chiasm, Paro... 48YesSegHead and neck cancer 531 StructSeg2019 Task 1[496] 20193DCTHead and Neck60YesSegHead and Neck Cancer 532 StructSeg2019 Task 2[496] 20193DCTHead and Neck60YesSeg Nasopharyngeal carcinoma 533 StructSeg2019 Task 3[496] 20193DCTChest60YesSegLung cancer 534 StructSeg2019 Task 4[496] 20193DCTLung60YesSegLung cancer 535 Learn2Reg Lung CT[497] 20203DCTThorax30YesRegRespiratory motion 536Learn2Reg NLST[69]20223DCTThorax420YesRegLung cancer 537 Learn2Reg Abdomen CT-CT[497] 20203DCTAbdomen50YesRegMulti-organ 538 Learn2Reg Abdomen MR-CT[497] 20213DCT/MRIAbdomen122YesRegMulti-modal 539 Continuous Registration[498] 20193DCT/MRLung/Brain142YesReg Respiratory mo- tion/COPD 540 Continuous Registration DIRLAB[499] 20183DCTLungs20YesReg/lmk Respiratory-induced motion 541 Continuous Registration EMPIRE[500] 20103DCTLungs60YesRegLung conditions 542HECKTOR 2020[501]20203DCT/ PETHead and Neck254YesSegHead and Neck Cancer 543HECKTOR 2021[502]20213DCT/ PETHead and Neck325YesSeg/ RegHead and Neck Cancer 544HECKTOR 2022[503]20223DCT/ PETHead and Neck883YesSeg/ RegHead and Neck Cancer 545VerSe19[504]20193DCTSpine160YesSeg/LabSpine diseases 546VerSe20[505]20203DCTSpine300YesSeg/LabSpine diseases 547CTSpine1K[506]20213DCTSpine1005YesSegSpine diseases 548CTPelvic1K[507]20203DCTPelvic1184YesSegPelvic bone conditions 549RibFrac2020[508]20203DCTRibs660YesSeg/Cls/DetRib fractures 550BTCV Abdomen[509]20153DCTAbdomen50YesSeg 551BTCV Cervical[509]20153DCTCervix50YesSegCervical cancer 552 ACRIN-HNSCC- FDG-PET-CT (ACRIN 6685)[510] 20163DCT/MR/NM/PETHead and Neck260YesClsHead and Neck Cancer 553 ACRIN-DSC-MR-Brain (ACRIN 6677)[511] 20192D/3DCT/MRBrain123YesClsGlioblastoma 554 ACRIN-FLT-Breast (ACRIN 6688)[512] 20173DCT/ PETBreast83YesClsBreast Cancer 555 ACRIN-FMISO-Brain (ACRIN 6684)[513] 20163DCT/MR/ PETBrain45YesSeg/ClsGlioblastoma 556 ACRIN-NSCLC-FDG- PET (ACRIN 6668)[511] 20203DCT/ PETLung242YesClsLung cancer 557AREN0532[514]20222D/3D CR/ CT/ MR/ PET/ RTIMAGE/ US Kidney544NoClsWilms tumor 558 AREN0532-Tumor- Annotations[515] 20233DCT/MRKidney543YesSegWilms’ Tumor 559AREN0533[516]20223D CR/ CT/ MR/ US Kidney294NoClsWilms tumor 560 AREN0533-Tumor- Annotations[517] 20233D CR/ CT/ MR/ US Kidney294YesSegWilms tumor 561AREN0534[518]20212D/ 3D CT/ MR/ PET/ US Kidney239YesSegWilms tumor 562AHOD0831[519]20222D/3D CR/ CT/ DX/ MR/ NM/ OT/ PET/ SC/ XA Lymphatic165YesSegHodgkin Lymphoma 563 AHOD0831-Tumor- Annotations[520] 20233DCT/ PET Lymph nodes, spleen, sali- vary glands, Waldeyer’... 165YesSegHodgkin Lymphoma 564HNSCC-3DCT-RT[521]20183DCTHead and Neck31YesSegHead and Neck Cancer 565HNSCC[522]20203DCT/MR/PETHead and Neck627YesSegHead and Neck Cancer 566 CT COLONOGRAPHY[523] 20153DCTColon825YesClsColon Cancer 567CT-Lymph-Nodes[524]20152D/3DCT Mediastinum / Abdomen 176YesDet/SegLymphadenopathy 568 C-Radiomics- Phantom[525] 20173DCTPhantom17NoClsLung cancer 569 C-Radiomics-Phantom- 2[526] 20193DCTPhantom251No-Phantom study 570 C-Radiomics-Phantom- 3[527] 20193DCTHead/Chest275YesSeg 144 571 C-Tumor Heterogeneity[528] 20233DCT/MR/ PETCervix23YesSeg/ClsCervical cancer 572TCGA-BLCA[529]20143D CT/ MR/ PET/ X-RAY Bladder120NoClsBladder carcinoma 573TCGA-COAD[530]20143DCTColon25NoClsColon cancer 574TCGA-ESCA[531]20143DCTEsophagus16NoClsEsophageal carcinoma 575TCGA-GBM[532]20143DCT/ DX/ MRBrain575NoCls Glioblastoma Multi- forme 576TCGA-HNSC[533]20143DCT/ MR/ PETHead and Neck479NoClsHead and Neck Cancer 577TCGA-KICH[534]20163DCT/MRKidney15NoClsKidney cancer 578TCGA-KIRC[535]20163DCR/CT/MRKidney2654YesClsKidney cancer 579 TCGA-KIRC- Radiogenomics[536] 20153DCT/MRKidney103YesClsRenal cell carcinoma 580TCGA-KIRP[534]20143DCT/ MR/ PETKidney33NoClsKidney cancer 581TCGA-LGG[537]20143DCT/MRBrain199YesClsLower Grade Glioma 582TCGA-LIHC[538]20142D/3DCT/MR/ PETLiver97NoClsLiver cancer 583TCGA-LUAD[539]20163DCT/ NM/ PTLung69NoClsLung cancer 584TCGA-LUSC[540]20163DCT/ PETLung37NoClsLung cancer 585TCGA-OV[541]20163DCT/ MR/ OTOvary143NoClsOvarian cancer 586 TCGA-OV- Radiogenomics[542] 20163DCTOvary93YesClsOvarian cancer 587 TCGA-OV- Proteogenomics[543] 20203DCTOvary20NoSegOvarian cancer 588TCGA-PRAD[533]20153DCT/MR/PETProstate14NoClsProstate cancer 589TCGA-READ[544]20143DCT/MRRectum3NoCls Rectum adenocarci- noma 590TCGA-SARC[545]20143DCT/MR Chest/ Ab- domen/ Leg/ Spine 33NoClsSarcoma 591TCGA-UCEC[538]20203D CT/ MR/ PET/ X-RAY Uterus65NoClsUterine cancer 592CPTAC-CCRCC[127]20182D/ 3D CT/ MI- CROSCOPY/ MR Kidney262YesClsRenal cell carcinoma 593CPTAC-CM[127]20182D/ 3DCT/ MR/ PTSkin95NoCls/ SegCutaneous Melanoma 594CPTAC-GBM[127]20182D/ 3D CT/ MI- CROSCOPY/ MR Brain200NoClsGlioblastoma 595CPTAC-HNSCC[127]20183DCT/MR/WSIHead and Neck207YesClsHead and Neck Cancer 596CPTAC-LSCC[127]20182D/3D CT/ MI- CROSCOPY/ PET Lung212NoClsLung cancer 597CPTAC-LUAD[546]20182D/ 3DCT/ MR/ PETLung244NoClsLung cancer 598CPTAC-PDA[547]20183D CT/ MR/ PET/ US Pancreas168NoClsPancreatic cancer 599CPTAC-SAR[548]20192D/ 3D CT/ MR/ PET/ US Abdomen/ Arm/ Bladder/ Chest/ Head–Neck/ Kidney/ Leg/... 88NoClsSarcomas 600CPTAC-UCEC[548]20192D/ 3D CT/ MR/ PET/ US Uterus250NoCls Endometrial Carci- noma 601NSCLC-Radiomics[549]20143DCTLung422YesSeg/ClsLung cancer 602 NSCLC-Radiomics- Genomics[549] 20143DCTLung89YesClsLung cancer 603 NSCLC- Radiogenomics[550] 20153DCT/ PETChest211YesSeg/ ClsLung cancer 604 NSCLC-Cetuximab (RTOG-0617)[551] 20183DCTChest490NoClsLung cancer 605 NSCLC-Radiomics- Interobserver1[552] 20193DCTLung22YesSegLung cancer 606CMB-CRC[553]20222D/3D CT/ MR/ PET/ US/ WSI Colon12NoClsColorectal Cancer 607CMB-GEC[553]20222D/3D CT/ MI- CROSCOPY/ MR/ PET Esophagus17NoSeg/Cls Gastroesophageal Cancer 608CMB-LCA[553]20222D/3D CT/ DX/ MR/ NM/ PT/ US Lung16NoClsLung cancer 609CMB-MEL[553]20222D/ 3D CT/ PET/ US/ WSI Skin40NoClsMelanoma 610CMB-MML[553]20222D/3D CR/ CT/ DX/ HISTOPATHOL- OGY/ MR/ PET/ XA Blood/Bone138NoClsMultiple Myeloma 611CMB-PCA[553]20222D/3D CT/ DX/ MR/ NM/ PET/ RF Prostate50NoClsProstate cancer 612Crowds-Cure-2017[554]20173DCT Lung, Kidney, Liver, Ovary 352YesSeg/DetCancer 613Crowds-Cure-2018[555]20193DCT Bladder/ Brain/ Colon/ Head- Neck/ Kidney/ Lung/ Pancr 324YesDet/RegMetastatic diseases 614QIN-Breast[556]20153DCT/ MR/ PETBreast68YesClsBreast cancer 145 615QIN-HEADNECK[557]20153DCT/ PETHead and Neck279YesSeg Head and neck carcino- mas 616QIN LUNG CT[558]20153DCTLung47NoClsLung cancer 617QIN-LungCT-Seg[559]20153DCTChest31YesSegLung cancer 618QIBA CT-1C[560]20113DCTPhantom1YesSeg 619QIBA-VolCT-1B[561]20203DCTLung40YesSegLung cancer 620 QIBA-CT-Liver- Phantom[562] 20213DCTLiver684NoSeg/DetLiver pathology 621 Multi-Modality Vertebra Recognition[563] 20152D/3DCT/MRSpine0YesLoc/Reg/RecSpine diseases 622xVertSeg[564]20163DCT Lumbar verte- brae 25YesSeg/ClsVertebral fractures 623 Computational Methods and Clinical Applications for Spine Imaging[565] 20153DCTSpine312YesSeg/LocSpinal diseases 624ACNS0332[566]20213DCT/MRBrain/Spine85YesSegBrain cancer 625AHEP0731[567]20213D CT/ MR/ PET/ US/ XA Liver/ Chest190NoSeg/ ClsLiver Cancer 626Anti-PD-1 Lung[568]20193DCT/ PET/ SCLung46NoClsLung cancer 627 Anti-PD-1 Immunother- apy Melanoma[569] 20193DCT/ MR/ PTSkin47NoClsMelanoma 628 BREAST- DIAGNOSIS[570] 20112D/ 3D CT/ MG/ MR/ PT Breast88YesClsBreast cancer 629CALGB50303[571]20213DCT/ PET Chest/ Ab- domen/ Pelvis 155YesCls Diffuse Large B-Cell Lymphoma 630 ELCAP Public Lung Image Database[572] 20033DCTLung50YesDetLung nodules 631GLIS-RT[573]20212D/3DCT/MRBrain230YesSeg/RegBrain tumors 632HEAR-EU[574]20173DCTCochlea80YesSeg Sensorineural hearing loss 633 Head-Neck Cetuximab (RTOG 0522)[575] 20133DCT/ PETHead and Neck111NoCls Head and Neck Carcinomas 634Head-Neck-PET-CT[576]20173DCT/ PETHead and Neck298YesSeg/ ClsHead and Neck Cancer 635 Head-Neck-Radiomics- HN1[549] 20193DCT/ PETHead/ Neck137YesSegHead and Neck Cancer 636LCTSC[577]20173DCTLung60YesSegLung cancer 637 LDCT-and-Projection- data[578] 20203DCT Head, Chest, Abdomen 299YesRecVarious pathologies 638LIDC-IDRI[579]20113DCTLung1018YesSeg/DetLung cancer 639Lung-PET-CT-Dx[580]20203DCT/ PETLung355YesCls/ DetLung cancer 640LungCT-Diagnosis[581]20143DCTLung61YesClsLung cancer 641 National Lung Screening Trial (NLST)[69] 20133D CT/ MI- CROSCOPY Chest26254YesClsLung cancer 642OPC-Radiomics[582]20203DCTHead and Neck606YesSeg/Cls Oropharyngeal Carci- noma 643 Parkinson’s Progression Markers Initiative (PPMI)[583] 20103D MR/ PET/ SPECT Brain683YesClsParkinson’s Disease 644Pediatric-CT-SEG[584]20213DCT Chest/ Ab- domen/ Pelvis 359YesSeg Non-cancer pediatric exams 645 Public Lung Database To Address Drug Response[572] 20093DCTLung93YesSeg/DetLung cancer 646QIDW[585]20153D CT/ MR/ PET/ US Lung52000NoClsQuality assurance 647 RIDER Lung PET- CT[134] 20153DCT/ PETLung243NoClsLung cancer 648SMIR Full Body CT[586]20133DCTSkeleton50NoSeg- 649 SPIE-AAPM Lung CT Challenge[587] 20153DCTLung70YesClsLung cancer 650 Vestibular-Schwannoma- SEG[588] 20213D MR/ RT- DOSE/ RTPLAN/ RTSTRUCT Brain242YesSeg Vestibular schwan- noma 651M-WHS[589]20173DCT/MRHeart120YesSegCardiac conditions 652Soft-tissue-Sarcoma[590]20153DCT/ MR/ PETExtremities51YesSeg/ClsSoft-tissue sarcoma 653Seg Soft Tissue[591]20213DCT/MR/PETSoft tissue51YesSeg Soft-tissue sarcomas (preprocessed) 654 Left Atrial Wall Thick- ness Challenge[592] 20163DCT/MRHeart20YesSegAtrial Fibrillation 655MELA22[593]20223DCTMediastinum1100YesDetMediastinal lesions 656 Head and Neck Auto Segmentation Challenge[495] 20153DCT Brainstem, Mandible, Chiasm, Optic Nerves, Paro... 48YesSeg Head and Neck Neoplasms 657 RSNA STR Pulmonary Embolism Detection[594] 20203DCTLung12195YesCls/DetPulmonary embolism 658KNIGHT[595]20213DCTKidney400YesClsRenal cancer 659 Data Science Bowl 2017[596] 20173DCTLungs2101YesClsLung cancer 660 OSIC Pulmonary Fibrosis Progression[597] 20203DCTLung200YesRegPulmonary fibrosis 146 661 The Visible Human Project[598] 19942D/3DCT/MRFull Body2NoAtlasAnatomical reference 662ABCs[599]20203DCT/MRBrain75YesSegGlioma 663MATCH[600]20212D/3DCT/X-RAYLung9YesLoc/TrkLung cancer 664CTVIE19[601]20193DCTLung64YesSegLung cancer 665 ImageCLEFtuberculosis 2018[602] 20183DCTChest1513YesClsTuberculosis 666 ImageCLEF 2017 Tuberculosis[603] 20173DCTChest944YesClsTuberculosis 667fastPET-LD[604]20213DCT/ PETWhole Body68YesDetOncologic Imaging 668Low Dose CT[605]20163DCTLiver30YesDetLiver lesions 669LUMIC[606]20183DCTLung13YesReg/Seg Pulmonary perfusion defects 670CAD-PE[607]20133DCTLiver91YesSegPulmonary Embolism 671ANODE09[608]20093DCTLung55YesDetLung cancer 672VESSEL12[609]20123DCTLung20YesSegRespiratory diseases 673RIDER Lung CT[610]20153DCTLung32YesSegLung cancer 674OpenKBP[611]20203DCTHead and Neck340YesRegHead and Neck Cancer 675 CT-vs-PET-Ventilation- Imaging[612] 20223DCT/ PETLung20NoClsLung cancer 676HCC-TACE-Seg[613]20213DCTLiver105YesSegLiver cancer 677 MOOD 2022- abdominal[614] 20223DCTAbdomen550YesSeg/ClsAnomalies 678 StageII-Colorectal- CT[615] 20223DCTAbdomen230NoClsColorectal cancer 679 Pseudo-PHI-DICOM- Data[616] 20212D/3D CT/MR/PET/X- RAY Various21NoRegVarious cancers 6804D-Lung[617]20163DCTLung20YesSegLung cancer 681Lung Phantom[618]20153DCTThorax1NoSegSynthetic lung nodules 682NaF PROSTATE[619]20133DCT/ PETProstate9NoClsProstate cancer 683 EndoVis 2022 - P2ILF 3D-CT[620] 20223DCT/ENDOSCOPYLiver15YesSegLiver Tumor 684 Finding and Measuring Lungs in CT Data[621] 20192D/3DCTLungs538YesSegLung diseases 685SynthRAD2023[622]20233DCT/MRBrain, Pelvis1080YesSynOncological 686IACTA-EST2023[623]20233DCTBrain402YesClsAcute Ischemic Stroke 687 ISBI2023 challenge - APIS[624] 20233DCTBrain96YesSeg 688AUTO-RTP[625]20223DCT Prostate, Sem- inal Vesicles, Pelvic Lymph Nodes 10YesSegProstate cancer 689Subsolid Nodules[626]20253DCTLung59YesSegLung cancer 690 DICOM-LIDC-IDRI- Nodules[627] 20183DCTLung875YesSegLung cancer 691 Burdenko-GBM- Progression[628] 20233DCT/MRBrain180YesSeg/ClsGlioblastoma 692 PDMR-833975-119- R[629] 20203DCT/MR/PETPancreas20NoCls Pancreatic adenocarci- noma 693APOLLO-5-LSCC[630]20213DCT/PETLung36YesSeg Lung squamous cell carcinoma 694APOLLO-5-LUAD[631]20213DCTLung5YesSegLung adenocarcinoma 695APOLLO-5-ESCA[632]20213DCTEsophagus4YesSeg Esophageal squamous cell carcinoma 696APOLLO-5-PAAD[633]20213DCTPancreas1YesSeg Pancreatic adenocarci- noma 697APOLLO-5-THYM[634]20213DCTThymus4YesSegThymoma 698 Lung-Fused-CT- Pathology[635] 20182D/3DCT/MICROSCOPYLung6YesSegLung cancer 699LUAD-CT-Survival[636]20173DCTLung (Thorax)40YesSegLung Adenocarcinoma 700ARAR0331[637]20223DCT/MR/ PETHead108YesSegNasopharyngeal cancer 701 GammaKnife- Hippocampal[638] 20223D CT/ MR/ RTSTRUCT Hippocampus390YesSegBrain conditions 702A091105[639]20233DCT/MRSoft tissue83NoSegDesmoid tumors 703 Colorectal-Liver- Metastases[640] 20233DCT/SEGLiver197YesSeg Colorectal cancer liver metastases 704 Head-Neck-CT- Atlas[641] 20173DCT/ MR/ PETHead/Neck215YesSegHead and neck cancer 705 NRG-1308 (RTOG 1308)[642] 20153DCTLung12YesSegLung cancer 706Phantom FDA[643]20153DCTLungs7NoRegLung cancer 707 CTpred-Sunitinib- panNET[644] 20223DCTPancreas38YesCls Pancreatic neuroen- docrine tumors 708 CALGB50303-Tumor- Annotations[520] 20233DCT/ PET Lymphatic system 155YesSeg/ Cls Diffuse Large B-Cell Lymphoma 709 RIDER-LungCT- Seg[645] 20203DCTLung31YesSegLung cancer 710 Radiomic-Feature- Standards[646] 20203DCTChest13YesSegLung cancer 711PleThora[647]20203DCTThoracic402YesSegLung cancer 712MRI-DIR[648]20183DCT/MRHead-Neck9NoRegHead and Neck Cancer 147 713 Ivy Glioblastoma Atlas Project (IvyGAP)[649] 20163DCT/MRBrain39YesSeg/ClsGlioblastoma 714RIDER Pilot[650]20232D/3DCR/CT/DXLung8NoClsLung cancer 715RADCURE[651]20243DCTHead and Neck3346YesSegHead and Neck Cancer 716 Adrenal-ACC-Ki67- Seg[652] 20233DCTAdrenal53YesSeg Adrenocortical carci- noma 717 Prostate-Anatomical- Edge-Cases[653] 20233DCT Prostate, Rectum, Bladder, Femoral Heads 131YesSegProstate Cancer 7183D-IRCADb[65]20103DCTLiver20YesSegLiver tumors 719SEG.A. 2023[654]20233DCTAorta56YesSegAortic diseases 720P2ILF[655]20222D/3DCT/MRLiver186YesSeg/RegLiver cancer 721SynthStrip[656]20222D/3DCT/MR/PETBrain622YesSegGlioblastoma 722MOOD[657]20243DCT/MRBrain/Abdomen1358YesDet/LocIncidental pathologies 723 PENGWIN2024- Task1[658] 20243DCTPelvis150YesSegPelvic fractures 724TriALS2024-Task1[659]20243DCTLiver201YesSegLiver tumors 725TriALS2024-Task2[660]20243DCTLiver240YesSeg Hepatocellular carci- noma 726 National Lung Screening Trial (NLST) 2D- Pathology[69] 20132D/3D CT/ MI- CROSCOPY Chest451YesClsLung cancer 727QUBIQ2021 3D CT[661]20213DCTPancreas118YesSegPancreatic lesions 728orCaScore[662]20143DCTHeart72YesDet/Seg Cardiovascular dis- eases 729INSPECT[663]20233DCTLungs23248YesClsPulmonary embolism 730BIMCV-R[664]20242D/3DCTThoracic8069YesRecMultiple diseases 731 Mandibular-CT- Dataset[665] 20183DCTMandible10YesSeg 732Semi-TeethSeg[666]20232D/3DCT/X-RAYTeeth38000YesSegDental issues Total:516,087+ Table 22: 3D MR datasets #DatasetYearDimModalityStructureVolumesLabelTaskDiseases 733TotalSegmentator MRI[667]20243DMR Whole- body 616YesSegVarious pathologies 734ACDC[668]20173DMRHeart150YesSeg/ ClsCardiac conditions 735M&Ms[669]20203DMRHeart375YesSegCardiac diseases 736M&Ms-2[670]20213DMRHeart360YesSegCardiac conditions 737LAScarQS 2022[671]20223DMRHeart194YesSeg/Quan Left atrial scar quantifi- cation and segmenta- tion 738LAScarQS++ 2024[672]20243DMRHeart200+YesSeg/Quan Multi-center left atrial and scar segmentation 739MyoPS2020[673]20203DMRHeart45YesSegMyocardial infarction 740MyoPS++ 2024[674]20243DMRHeart200+YesSeg Multi-center myocar- dial pathology 741M-WHS[589]20173DCT/MRHeart120YesSegCardiac conditions 742WHS++ 2024[675]20243DCT/MRHeart200+YesSeg Multi-center whole heart segmentation 743 CuRIOUS2018-MR FLAIR[676] 20183DMR/ USBrain33YesRegBrain tumors 744CuRIOUS2018-US[77]20183DMR/ USBrain32YesRegBrain tumors 745CuRIOUS2018-MR T1W[77]20183DMR/ USBrain33YesRegBrain tumor 746 CuRIOUS2019-MR- FLAIR[677] 20193DMR/ USBrain32YesRegLow-grade gliomas 747CuRIOUS2019[77]20193DMR/ USBrain33YesRegBrain tumor 748CuRIOUS2019 US[677]20193DMR/ USBrain33YesRegLow-grade gliomas 749 CuRIOUS2019-MR T1W[677] 20193DMR/ USBrain33YesRegBrain tumor 750CuRIOUS2022[678]20223DMR/ USBrain33YesSegLow-grade gliomas 751CrossMoDA2021[679]20213DMRBrain347YesSeg Vestibular Schwan- noma 752CrossMoDA2022[679]20223DMRBrain347YesSeg/ Cls Vestibular Schwan- noma 753CrossMoDA2023[679]20233DMRBrain544YesSeg Vestibular Schwan- noma 754OASIS-1[680]20073DMRBrain416YesSeg/ ClsAlzheimer’s Disease 755OASIS-2[681]20103DMRBrain1200YesClsAlzheimer’s Disease 756OASIS-3[74]20193DCT/MR/ PETBrain5699YesSeg/ClsAlzheimer’s Disease 757Learn2Reg OASIS[497]20203DMRBrain416YesRegAlzheimer’s Disease 758 Learn2Reg Hippocampus MR[469] 20203DMRBrain263YesReg Anatomical segmenta- tion 759Learn2Reg LUMIR[497]20243DMR/USBrain269YesRegMulti-modal 760PROMISE09[682]20093DMRProstate15YesSegProstate cancer 761PROMISE12[683]20123DMRProstate50YesSegProstate cancer 762Prostate-3T[684]20133DMRProstate64YesSegProstate cancer 148 763Prostate-Diagnosis[685]20153DMRProstate92YesSeg/ ClsProstate cancer 764PROSTATEx[686]20163DMRProstate204YesClsProstate cancer 765PROSTATEx-2[687]20173DMRProstate162YesClsProstate cancer 766QIN Breast DCE-MR[688]20143DMRBreast10YesSegBreast cancer 767QIN-SARCOMA[689]20143DMR Breast, Calf, Chest, Elbow, Knee, Leg, Shoulder... 15NoClsSoft-tissue sarcoma 768 QIN GBM Treatment Response[690] 20152D/ 3DMRBrain54NoCls Glioblastoma Multi- forme 769QIN-BRAIN-DSC-MR[691]20163DMRBrain49YesSegGlioma 770 QIN-PROSTATE- Repeatability[692] 20183DMRProstate15YesSegProstate cancer 771QIN-BREAST-02[693]20193DMRBreast13NoClsBreast cancer 772Prostate-MR-US-Biopsy[694]20203DMR/ USProstate1151YesReg/SegProstate Cancer 773 Prostate MR Segmentation Dataset[695] 20203DMRProstate116YesSegProstate cancer 774PROSTATEx-Seg-Zones[696]20203DMRProstate98YesSegProstate cancer 775PROSTATEx-Seg-HiRes[697]20203DMRProstate66YesSegProstate cancer 776PI-CAI[698]20223DMRProstate1500YesSeg/ ClsProstate cancer 777μ-RegPro2023[699]20233DMR/ USProstate108YesReg/lmkProstate cancer 778SKI10[700]20103DMR Bone, Cartilage 150YesSegOsteoarthritis 779TADPOLE[701]20173DMR/ PETBrain1667YesCls/ RegAlzheimer’s Disease 780MSSEG-2[702]20213DMRBrain100YesSegMultiple sclerosis 781MSSEG 2016[703]20162D/ 3DMRBrain15YesSegMultiple sclerosis 782MSSEG 2008[704]20083DMRBrain38YesSegMultiple Sclerosis 783Cam-CAN[705]20153DMEG/ MRBrain3000NoClsHealthy ageing 784ISLES 2015[706]20153DMRBrain114YesSegIschemic stroke 785ISLES 2016[707]20163DMRBrain75YesSegIschemic stroke 786ISLES 2017[707]20173DMRBrain75YesSegIschemic stroke 787ISLES 2018[708]20183DMRBrain103YesSegIschemic stroke 788ISLES 2022[709]20223DMRBrain400YesSegIschemic stroke 789ISLES 2024[492]20243DMRBrain250YesSeg Acute/subacute ischemic stroke 790WMH[710]20173DMRBrain170YesSeg White matter hyperin- tensities 791BraTS 2012[73]20123DMRBrain50YesSegGlioma 792BraTS 2013[73]20133DMRBrain60YesSegGlioma 793BraTS 2014[73]20143DMRBrain238YesSegGlioma 794BraTS 2015[73]20153DMRBrain253YesSeg/ProgGlioma 795BraTS 2016[711]20163DMRBrain391YesSeg/ProgGlioma 796BraTS 2017[537]20173DMRBrain477YesSeg/SurvGlioma 797BraTS 2018[73]20183DMRBrain542YesSeg/SurvGlioma 798BraTS 2019[73]20193DMRBrain626YesSeg/SurvGlioma 799BraTS 2020[712]20203DMRBrain660YesSeg/SurvGlioma 800BraTS 2021[713]20213DMRBrain2040YesSeg/MGMTGlioma 801BraTS 2022[537]20223DMRBrain1470YesSegGlioma 802BraTS 2023[714]20233DMRBrain5880YesSegGlioma 803MSD01 BrainTumour[469]20183DMRBrain750YesSegBrain tumor 804MSD02 Heart[469]20183DMRHeart30YesSegCardiac conditions 805MSD04 Hippocampus[469]20183DMRHippocampus394YesSeg Anatomical segmenta- tion 806MSD05 Prostate[469]20183DMRProstate48YesSegProstate cancer 807FeTA 2021[715]20213DMRBrain50YesSegCongenital Disorders 808FeTA 2022[716]20223DMRBrain80YesSeg Neurodevelopment disorders 809fastMR[717]20202D/ 3DMR Brain/ Knee/ Prostate/ Breast 1594NoRecMulti-organ 810High Anisotropy MR[718]20133DMRSpine17NoRecSpine conditions 811 1000 Functional Connectomes Project[719] 20103DMRBrain1414NoClsAdult ADHD 812 Language Processing Children[720] 20223DMRBrain322YesClsLanguage impairment 813ISPY1 (ACRIN 6657)[721]20163DMRBreast847YesSeg/ ClsBreast cancer 814 ACRIN-Contralateral-Breast- MR[722] 20213DCR/MRBreast984NoClsBreast Cancer 815 ACRIN 6698/I-SPY2 Breast DWI[723] 20213DMRBreast385YesSeg/ ClsBreast cancer 816ADNI[76]20173DMR/ PETBrain2500NoClsAlzheimer’s Disease 817ADNIDOD[724]20173DMR/ PETBrain195NoClsAlzheimer’s Disease 818ABVIB[725]20173DMRBrain280NoClsAlzheimer’s Disease 819AIBL[726]20173DMR/ PETBrain278YesClsAlzheimer’s Disease 820AOMIC-ID1000[727]20213DMRBrain928NoRegHealthy adults 821BOLD Verb Generation[728]20204DMRBrain143NoReg Developmental lan- guage disorder 149 822 Brain Correlates of Math Development[729] 20183DMRBrain132NoCls Developmental disor- ders 823 Brain Segmentation Testing Protocol[730] 20113DMRBrain312YesSegAlzheimer’s Disease 824BrainMetShare[731]20203DMRBrain156YesSegBrain metastases 825Breast-MR-NACT-Pilot[732]20163DMRBreast64YesSegBreast cancer 826 CAMR Rat Brain MR Data[733] 20203DMRBrain264NoRegAnimal model study 827Caltech Conte Center[734]20223DMRBrain117NoClsHealthy 828Cognitive Training[735]20203DMRBrain166NoCls Cognitive training study 829 Colin 3T/7T High-resolution Atlas[736] 20143DMRBrain19NoRegBrain diseases 830 Concrete Permuted Rule Operations[737] 20213DMRBrain96NoClsCognitive function 831 Cortical Myelin T1w/T2w[738] 20213DMRBrain86NoCls Unipolar depressive disorders 832 Cross-Sectional Multidomain Lexical Processing[739] 20193DMRBrain91NoCls Language processing study 833 Prefrontal Cortex Development[740] 20213DMRBrain90NoClsDevelopmental study 834Duke-Breast-Cancer-MR[741]20213DMRBreast922YesSeg/ ClsInvasive breast cancer 835Dynamic Passive Threat[742]20193DMRBrain295200NoCls Threat processing study 836 Emotion Regulation Ageing Brain[743] 20203DMRBrain34NoClsNormal ageing 837Resting State Bilinguals[744]20193DMRBrain823NoClsHealthy adults 838 Brain Genomics Superstruct Project (GSP)[745] 20153DMRBrain1570NoSegHealthy adults 839 Human Connectome Project (HCP)[75] 20173DMRBrain1206NoCls/RecHealthy young adults 840IXI Dataset[746]20243DMRBrain600NoReg/RecHealthy adults 841KNOAP2020[747]20202D/ 3DMR/X-RAYKnee453YesClsKnee Osteoarthritis 842LGG-1p19qDeletion[748]20173DMRBrain159YesSeg/ ClsLow Grade Glioma 843 Lausanne TOF-MRA Aneurysm Cohort[749] 20213DMRBrain284YesDetBrain aneurysm 844MASiVar[750]20213DMRBrain319NoTrkHealthy subjects 845MIRIAD dataset[751]20133DMRBrain708NoRegAlzheimer’s Disease 846 MPI-Leipzig Mind-Brain- Body[752] 20193DEEG/MRBrain318NoClsHealthy cohort 847 Cue Induced Craving MR[753] 20203DMRBrain598NoRec Normal neurophysio- logical states 848 Children Adults Animated Film MR[754] 20183DMRBrain155NoRecDevelopmental study 849Mouse rest multicentre[755]20193DMRBrain255NoClsHealthy mouse model 850Multi-echo Cambridge[756]20183DMRBrain89NoRecHealthy neurotypical 851NARPS[757]20193DMRBrain108NoClsHealthy participants 852Narratives[758]20193DMRBrain891NoClsHealthy participants 853 Naturalistic Neuroimaging Database[759] 20213DMRBrain86NoClsHealthy controls 854 Neurocognitive aging data release with behavioral[760] 20223DMRBrain301NoCls Healthy cognitive aging 855PETfrog[761]20203DMR/ PETBrain238NoClsBrain development 856Pragmatic Language[762]20213DMRBrain145NoLoc Pragmatic comprehen- sion deficits 857REMBRANDT[763]20143DMRBrain130YesSeg/ ClsGliomas 858SIMON Dataset[764]20193DMRBrain73NoRegHealthy Control 859SUDMEX CONN[765]20213DMRBrain138NoClsCocaine use disorder 860 Serum Grey Matter Cortical Thickness[766] 20203DMRBrain143NoReg Brain morphometry study 861 Speech disfluencies: Neuro- physiological aspect in normal population[767] 20213DMRBrain81NoClsSpeech disorders 862 T1 Chronotype Sleep Study[768] 20213DMRBrain136NoClsHealthy 863 TCGA-GBM-QI- Radiogenomics[769] 20143DMRBrain55YesSegGlioblastoma 864 TCGA-GBM- Radiogenomics[769] 20143DMRBrain75YesSegGlioblastoma 865TCGA-BRCA[770]20142D/ 3DMG/MRBreast139NoClsBreast cancer 866TCGA-CESC[531]20143DMRCervix54NoClsCervical cancer 867 TCGA-Breast- Radiogenomics[771] 20153DMRBreast84YesSeg/ ClsBreast cancer 868BraTS-TCGA-GBM[537]20173DMRBrain135YesSegGlioma 869BraTS-TCGA-LGG[537]20173DMRBrain108YesSegGlioma 870TCGA-LGG-Mask[537]20173DMRBrain188YesSegLow Grade Glioma 871 Stockholm Sleepy Brain Study[772] 20183DMRBrain84NoClsSleep deprivation 872 Harm Avoidance Gray Matter[773] 20163DMRBrain95NoClsPersonality traits 873Human Voice Areas[774]20153DMRBrain218YesClsHealthy 150 874 UCLA Consortium for Neuropsychiatric Phenomics LA5c Study[775] 20163DMRBrain273NoCls Neuropsychiatric Disorders 875 Washington University 120[776] 20173DMRBrain120NoRestHealthy young adults 876 White matter deficits in co- caine use disorder V1.0[777] 20213DMRBrain133NoClsCocaine Use Disorder 877 Working memory in healthy and schizophrenic individuals[778] 20163DMRBrain99NoClsSchizophrenia 878 rsfMR comorbidity SmokingandSchizophrenia[779] 20183DMRBrain92YesCls Schizophrenia and nicotine dependence 879IVDM3Seg[780]20183DMR Intervertebral Discs 96YesSeg/LocSpine diseases 880MRNet[781]20183DMRKnee1370YesCls Knee MR abnormali- ties 881EMIDEC[782]20203DMRHeart150YesSeg/ ClsMyocardial infarction 882iSeg2017[783]20173DMRBrain23YesSeg Neurodevelopmental conditions 883 UW-Madison GI Tract Image Segmentation[784] 20223DMR Stomach, Small Bowel, Large Bowel 467YesSeg Gastrointestinal cancers 884MUDI2019[785]20193DMRBrain1344YesRecHealthy 885iSeg-2019[786]20193DMRBrain39YesSegHealthy development 886 Longitudinal Multiple Sclero- sis Lesion Segmentation[787] 20153DMRBrain82YesSegMultiple sclerosis 887COSMOS 2022[788]20223DMR Carotid Artery 75YesSegAtherosclerosis 888cSeg-2022[789]20223DMRCerebellum33YesSegNormal development 889 Brain Tumor Progression Prediction[790] 20213DMRBrain40YesClsBrain cancer 890 Heart Segmentation in MR Images 20213DMRHeart30YesSegCardiac conditions 891VWS 2021[791]20213DMR Carotid Arteries 50YesSegAtherosclerosis 892 Atrial Segmentation Challenge[792] 20183DMRHeart154YesSegAtrial fibrillation 893 IronTract Challenge 2019[793] 20193DMRBrain2YesTrkAnatomical structure 894DiSCo 2021[794]20213DMRBrain3YesRegTractography challenge 895RealNoiseMR 2021[795]20213DMRBrain25YesRecDenoising challenge 896MOOD 2022-brain[657]20223DMRBrain800YesCls/LocGeneral pathologies 897AAPM-RT-MAC[796]20193DMRHead-Neck55YesSegHead and Neck Cancer 898HVSMR 2016[797]20163DMRHeart20YesSeg Congenital heart disease 899MRBrainS13[71]20133DMRBrain20YesSeg Age-related brain conditions 900 HARDI Reconstruction Challenge Dataset[798] 20133DMRBrain6YesRecDiffusion imaging 901CAUSE07[799]20073DMRBrain38YesSegNeurological Disorders 902PROMISE09[682]20093DMRProstate15YesSegProstate cancer 903ISMRM2015[800]20153DMRBrain34YesRec/TrkTractography challenge 904Where is VALDO?[801]20213DMRBrain306YesSeg/Det/Loc Cerebral Small Vessel Disease 905NEATBrainS15[71]20153DMRBrain20YesSeg Age-related brain conditions 906MRBrainS18[802]20183DMRBrain30YesSeg Diabetes, Dementia, Alzheimer’s 907STACOM 2011[803]20113DMR/ USHeart1158YesReg/TrkHealthy volunteers 9083T Brain-Behavior MR[804]20143DMRBrain36NoRegHealthy subjects 909 Connectivity Test-Retest MR[805] 20153DMRBrain342NoTrkHealthy volunteers 910Mindboggle-101[806]20123DMRBrain101YesSeg Anatomical segmenta- tion 911 Individual Brain Charting (IBC)[807] 20203DMRBrain600YesClsHealthy 912Raider[808]20153DMRBrain11NoClsHealthy 913 Diffusion MR Data Harmonisation[809] 20173DMRBrain14YesReg Cross-scanner harmo- nization 914MEMENTO[810]20193DMRBrain1536YesReg Neurodegenerative diseases 915MUDI2019[785]20193DMRBrain6720NoReg/RecMicrostructure imaging 916CMRxMotion[811]20223DMRHeart360YesSeg/ ClsNot specified 917PDMR-BL0293-F563[812]20193DMRLiver/Bone19NoCls Bladder cancer metas- tasis 918PDMR-292921-168-R[629]20203DMR/SRAbdomen20NoCls Pancreatic adenocarci- noma 919PDMR-997537-175-T[629]20203DMR/SRColon24NoClsColon adenocarcinoma 920PDMR-425362-245-T[629]20213DMR/SRAbdomen20NoClsMelanoma 151 921PDMR-521955-158-R4[813]20223DMR/SR Pancreas/ Lung 20NoCls Pancreatic adenocarci- noma 922ICDC-Glioma[814]20212D/ 3D MICROSCOPY/ MR Brain78NoSegGlioma 923 Generation R Pediatric MR Resources[815] 20143DMRBrain666NoCls Normative develop- mental 924 High-quality diffusion- weighted imaging of Parkin- son’s disease[816] 20143DMRBrain53NoClsParkinson’s disease 925 MGH Neonatal/Pediatric ADC Atlases[817] 20153DMRBrain201YesRegAcute brain injury 926RIDER Phantom MR[818]20113DMRPhantom10NoQAPhantom study 927RIDER Breast MR[819]20113DMRBreast40NoClsBreast cancer 928ATLAS2023[820]20233DMRLiver90YesSeg Hepatocellular carci- noma 929SMILE-UHURA2023[821]20233DMRBrain25YesSeg Cerebral Small Vessel Diseases 930CMRxRecon[822]20232D/ 3DMRHeart300YesRec/SegCardiac diseases 931CAS202320233DMRBrain150YesSeg Anatomical segmenta- tion 932CROWN2023[823]20233DMRBrain600YesCls/ RegMultiple pathologies 933QuantConn[824]20233DMRBrain206YesRec Microstructure quan- tification 934BONBID-HIE2023[825]20233DMRBrain133YesSeg Hypoxic Ischemic Encephalopathy 935LLD-MMR2023[826]20233DMRLiver498YesClsLiver diseases 936WBMR-NF[827]20233DMR Whole- body 400YesSeg/DetNeurofibromatosis 937SLCN[828]20233DMRBrain514YesReg/Cls Neurodevelopmental disorders 938SPPIN2023[829]20233DMRAbdomen96YesSegNeuroblastoma 939Shifts Challenge 2022[830]20223DMRBrain172YesSegMultiple sclerosis 940Mouse-Astrocytoma[831]20173DMRBrain48NoCls Glioblastoma Multi- forme 941 GBM-MR-NER- Outcomes[832] 20143DMRBrain45NoRegGlioblastoma 942UPENN-GBM[833]20223DMRBrain3680YesSegGlioblastoma 943 ISPY1-Tumor-SEG- Radiomics[834] 20223DMRBreast163YesSegBreast cancer 944I-SPY2 Trial[835]20223DMRBreast719YesSeg/ ClsBreast cancer 945IvyGAP-Radiomics[836]20203DMRBrain37YesSegGlioblastoma 946Brain-TR-GammaKnife[837]20233DMRBrain47YesSeg/ ClsBrain cancer 947ExACT[838]20233DMRAnus30YesCls/LocAnal cancer 948 ACNS0332-Tumor- Annotations[839] 20223DMRBrain85YesSegBrain tumor 949UCSF-PDGM[840]20223DMRBrain501YesSegDiffuse Gliomas 950RHUH-GBM[841]20233DMRBrain40YesSegGlioblastoma 951RIDER Neuro MR[842]20113DMRBrain19NoClsBrain cancer 952 Meningioma-SEG- CLASS[843] 20233DMRBrain96YesSeg/ Cls Intracranial menin- giomas 953Mouse-Mammary[831]20153DMRMammary32NoClsBreast Cancer 954 MRQy-Quality- Measures[844] 20203DMR Brain, Cervix 233NoQA Brain and Cervical Cancers 955DICOM-Glioma-SEG[845]20203DMRBrain167YesSegBrain cancer 956ADAM2020[846]20203DMRBrain255YesSeg/Det/ClsIntracranial aneurysms 957CSI15[847]20152D/ 3DMR/X-RAYSpine345YesCls/SegSpine conditions 958LPBA40[848]20013DMRBrain40YesSegHealthy 959 Continuous Registration ISBR18[849] 20183DMRBrain18YesSegRegistration challenge 960CUMC12[850]20183DMR Head and Neck 18YesSegNot specified 961MGH10[497]20183DMRBrain10YesSeg Anatomical segmenta- tion 962BrainPTM 2021[851]20213DMRBrain75YesSegBrain tumors 963OpenMind[72]20243DMR Head and Neck 114570YesSeg/RecHealth Status Total:523,847+ Table 23: 3D US datasets #DatasetYearDimModalityStructureVolumesLabelTaskDiseases 964TDSC-ABUS2023[852]20233DUSBreast200YesSeg/ Cls/ DetBreast cancer 965CETUS2014[853]20143DUSHeart45YesSegCardiac conditions 966 MVSeg- 3DTEE2023[854] 20233DUSMitral valve175YesSegMitral valve disease 967AREN0532[514]20222D/3D US/ CR/ CT/ MR/ PET/ RTIMAGE Kidney544NoClsWilms tumor 968AREN0533[516]20223DUS/CR/CT/MRKidney294NoClsWilms tumor 152 969 AREN0533-Tumor- Annotations[517] 20233DUS/CR/CT/MRKidney294YesSegWilms tumor 970AREN0534[518]20212D/3DUS/ CT/ MR/ PETKidney239YesSegWilms tumor 971CPTAC-PDA[547]20183DUS/ CT/ MR/ PETPancreas168NoClsPancreatic cancer 972CPTAC-SAR[548]20192D/3DUS/ CT/ MR/ PET Abdomen/ Arm/ Bladder/ Chest/ Head–Neck/ Kidney/ Leg/ ... 88NoClsSarcomas 973CPTAC-UCEC[548]20192D/3DUS/ CT/ MR/ PETUterus250NoCls Endometrial Carcinoma 974CMB-CRC[553]20222D/3D US/ CT/ MR/ PET/ WSI Colon12NoClsColorectal Cancer 975CMB-LCA[553]20222D/3D US/ CT/ DX/ MR/ NM/ PT Lung16NoClsLung cancer 976CMB-MEL[553]20222D/3D US/ CT/ PET/ WSI Skin40NoClsMelanoma 977AHEP0731[567]20213D US/ CT/ MR/ PET/ XA Liver/Chest190NoSeg/ClsLiver Cancer 978QIDW[585]20153DUS/ CT/ MR/ PETLung52000NoQAQuality assurance 979 CuRIOUS2018-MR FLAIR[676] 20183DUS/MRBrain33YesRegBrain tumors 980CuRIOUS2018-US[77]20183DUS/MRBrain32YesRegBrain tumors 981 CuRIOUS2018-MR T1W[77] 20183DUS/MRBrain33YesRegBrain tumor 982 CuRIOUS2019-MR- FLAIR[677] 20193DUS/MRBrain32YesRegLow-grade gliomas 983CuRIOUS2019[77]20193DUS/MRBrain33YesRegBrain tumor 984CuRIOUS2019 US[677]20193DUS/MRBrain33YesRegLow-grade gliomas 985 CuRIOUS2019-MR T1W[677] 20193DUS/MRBrain33YesRegBrain tumor 986CuRIOUS2022[678]20223DUS/MRBrain33YesSegLow-grade gliomas 987 Learn2Reg LUMIR[497] 20243DUS/MRBrain269YesRegMulti-modal 988 Prostate-MR-US- Biopsy[694] 20203DUS/MRProstate1151YesReg/SegProstate Cancer 989μ-RegPro2023[699]20233DUS/MRProstate108YesReg/lmkProstate cancer 990STACOM 2011[803]20113DUS/MRHeart1158YesReg/TrkHealthy volunteers Total:56,609+ Table 24: 3D PET datasets #DatasetYearDimModalityStructureVolumesLabelTaskDiseases 991QIN PET Phantom[855]20143DPETBody2YesSegPhantom study 992AutoPET[448]20223DPET/CTWhole-body1014YesSegCancer 993AutoPET I[449]20233DPET/CTWhole-body1219YesSegCancer 994COVID-19-NY-SBU[481]20212D/ 3D PET/CT/ MR/ X-RAY Brain/ Chest1384NoClsCOVID-19 995HECKTOR 2020[501]20203DPET/CTHead and Neck254YesSegHead and Neck Cancer 996HECKTOR 2021[502]20213DPET/CTHead and Neck325YesSeg/ RegHead and Neck Cancer 997HECKTOR 2022[503]20223DPET/CTHead and Neck883YesSeg/ RegHead and Neck Cancer 998 ACRIN-HNSCC- FDG-PET-CT (ACRIN 6685)[510] 20163DPET/CT/MR/NMHead and Neck260YesClsHead and Neck Cancer 999 ACRIN-FLT-Breast (ACRIN 6688)[512] 20173DPET/CTBreast83YesClsBreast Cancer 1000 ACRIN-FMISO-Brain (ACRIN 6684)[513] 20163DPET/CT/MRBrain45YesSeg/ClsGlioblastoma 1001 ACRIN-NSCLC-FDG- PET (ACRIN 6668)[511] 20203DPET/CTLung242YesClsLung cancer 1002AREN0532[514]20222D/3D PET/CR/ CT/ MR/ RTIMAGE/ US Kidney544NoClsWilms tumor 1003AREN0534[518]20212D/ 3DPET/CT/ MR/ USKidney239YesSegWilms tumor 1004AHOD0831[519]20222D/3D PET/CR/ CT/ DX/ MR/ NM/ OT/ SC/ XA Lymphatic165YesSegHodgkin Lymphoma 1005 AHOD0831-Tumor- Annotations[520] 20233DPET/CT Lymph nodes, spleen, sali- vary glands, Waldeyer’... 165YesSegHodgkin Lymphoma 1006HNSCC[522]20203DPET/CT/MRHead and Neck627YesSegHead and Neck Cancer 1007 C-Tumor Heterogeneity[528] 20233DPET/CT/MRCervix23YesSeg/ClsCervical cancer 1008TCGA-BLCA20143D PET/CT/ MR/ X-RAY Bladder120NoClsBladder carcinoma 1009TCGA-HNSC[533]20143DPET/CT/ MRHead and Neck479NoClsHead and Neck Cancer 1010TCGA-KIRP[534]20143DPET/CT/ MRKidney33NoClsKidney cancer 1011TCGA-LIHC[538]20142D/3DPET/CT/MRLiver97NoClsLiver cancer 1012TCGA-LUSC20163DPET/CTLung37NoClsLung cancer 1013TCGA-PRAD[533]20153DPET/CT/MRProstate14NoClsProstate cancer 153 1014TCGA-UCEC[538]20203D PET/CT/ MR/ X-RAY Uterus65NoClsUterine cancer 1015CPTAC-LSCC[127]20182D/3D PET/CT/ MI- CROSCOPY Lung212NoClsLung cancer 1016CPTAC-LUAD[546]20182D/ 3DPET/CT/ MRLung244NoClsLung cancer 1017CPTAC-PDA[547]20183DPET/CT/ MR/ USPancreas168NoClsPancreatic cancer 1018CPTAC-SAR[548]20192D/ 3DPET/CT/ MR/ US Abdomen/ Arm/ Bladder/ Chest/ Head–Neck/ Kidney/ Leg/... 88NoClsSarcomas 1019CPTAC-UCEC[548]20192D/ 3DPET/CT/ MR/ USUterus250NoCls Endometrial Carci- noma 1020 NSCLC- Radiogenomics[550] 20153DPET/CTChest211YesSeg/ ClsLung cancer 1021CMB-CRC[553]20222D/3D PET/CT/ MR/ US/ WSI Colon12NoClsColorectal Cancer 1022CMB-GEC[553]20222D/3D PET/CT/ MI- CROSCOPY/ MR Esophagus17NoSeg/Cls Gastroesophageal Cancer 1023CMB-MEL[553]20222D/ 3DPET/CT/ US/ WSISkin40NoClsMelanoma 1024CMB-MML[553]20222D/3D PET/CR/ CT/ DX/ HISTOPATHOL- OGY/ MR/ XA Blood/Bone138NoClsMultiple Myeloma 1025CMB-PCA[553]20222D/3D PET/CT/ DX/ MR/ NM/ RF Prostate50NoClsProstate cancer 1026QIN-Breast[556]20153DPET/CT/ MRBreast68YesClsBreast cancer 1027QIN-HEADNECK[557]20153DPET/CTHead and Neck279YesSeg Head and neck carcino- mas 1028AHEP0731[567]20213D PET/CT/ MR/ US/ XA Liver/ Chest190NoSeg/ ClsLiver Cancer 1029Anti-PD-1 Lung[568]20193DPET/CT/ SCLung46NoClsLung cancer 1030CALGB50303[571]20213DPET/CT Chest/ Abdomen/ Pelvis 155YesCls Diffuse Large B-Cell Lymphoma 1031 Head-Neck Cetuximab (RTOG 0522)[575] 20133DPET/CTHead and Neck111NoCls Head and Neck Carcinomas 1032Head-Neck-PET-CT[576]20173DPET/CTHead and Neck298YesSeg/ ClsHead and Neck Cancer 1033 Head-Neck-Radiomics- HN1[549] 20193DPET/CTHead/ Neck137YesSegHead and Neck Cancer 1034Lung-PET-CT-Dx[580]20203DPET/CTLung355YesCls/ DetLung cancer 1035 Anti-PD-1 Immunotherapy Melanoma[569] 20193DPET/CT/MRSkin47NoClsMelanoma 1036 BREAST- DIAGNOSIS[570] 20112D/ 3DPET/CT/MG/MRBreast88YesClsBreast cancer 1037 Parkinson’s Progres- sion Markers Initiative (PPMI)[583] 20103DPET/MR/SPECTBrain683YesClsParkinson’s Disease 1038QIDW[585]20153DPET/CT/MR/USLung52000NoQAQuality assurance 1039 RIDER Lung PET- CT[134] 20153DPET/CTLung243NoClsLung cancer 1040Soft-tissue-Sarcoma[590]20153DPET/CT/MRExtremities51YesSeg/ClsSoft-tissue sarcoma 1041Seg Soft Tissue[591]20213DPET/CT/MRSoft tissue51YesSeg Soft-tissue sarcomas (preprocessed) 1042fastPET-LD[604]20213DPET/CTWhole Body68YesDetOncologic Imaging 1043 CT-vs-PET-Ventilation- Imaging[612] 20223DPET/CTLung20NoClsLung cancer 1044NaF PROSTATE[619]20133DPET/CTProstate9NoClsProstate cancer 1045 Pseudo-PHI-DICOM- Data[616] 20212D/3D PET/CT/MR/X- RAY Various21NoRegVarious cancers 1046 PDMR-833975-119- R[629] 20203DPET/CT/MRPancreas20NoCls Pancreatic adenocarci- noma 1047APOLLO-5-LSCC[630]20213DPET/CTLung36YesSeg Lung squamous cell carcinoma 1048ARAR0331[637]20223DPET/CT/MRHead108YesSegNasopharyngeal cancer 1049 CALGB50303-Tumor- Annotations[520] 20233DPET/CTLymphatic system155YesSeg/ Cls Diffuse Large B-Cell Lymphoma 1050OASIS-3[74]20193DPET/CT/MRBrain5699YesSeg/ClsAlzheimer’s Disease 1051TADPOLE[701]20173DPET/MRBrain1667YesCls/ RegAlzheimer’s Disease 1052ADNI[76]20173DPET/MRBrain2500NoClsAlzheimer’s Disease 1053ADNIDOD[724]20173DPET/MRBrain195NoClsAlzheimer’s Disease 1054AIBL[726]20173DPET/MRBrain278YesClsAlzheimer’s Disease 1055PETfrog[761]20203DPET/MRBrain238NoClsBrain development Total:95,456+ Table 25: 3D Other datasets #DatasetYearDimModalityStructureVolumesLabelTaskDiseases 1056MitoEM[856]20203D3D MICROSCOPYBrain2YesSeg Mitochondrial ultrastructure 10573D Platelet EM[857]20213D3D MICROSCOPYPlatelet2YesSeg Platelet ultrastruc- ture 154 1058PCa Bx 3Dpathology[858]20233D3D MICROSCOPYProstate50YesSeg/ LocProstate cancer 1059CADA[859]20203D3D DSABrain131YesDet/ Seg/ ClsCerebral aneurysms 1060 ISBI2023 challenge - SHINY- ICARUS[860] 20223D3D DSA Internal Carotid Artery 82YesSeg Internal carotid artery aneurysms 1061CADA-AS[861]20203D3D DSABrain110YesSegCerebral aneurysms 1062CADA-RRE[861]20203D3D DSABrain131YesSeg/ClsCerebral aneurysm 1063XPRESS2023[862]20233D3D X-RAYBrain3YesSegNeuroanatomy 1064 Learn2Reg ThoraxCBCT[617] 20233DCBCT/FBCTThorax40YesRegLung cancer 1065 Pancreatic-CT-CBCT- SEG[863] 20213DCBCT/CTPancreas40YesSegPancreatic cancer 1066Pelvic-Reference-Data[864]20193DCBCT/CTPelvis58YesRegProstate cancer 1067ToothFairy2023[865]20233DCBCT Inferior Alveolar Nerve 443YesSeg Dental surgery planning 1068RETOUCH[866]20173DOCTRetina112YesSegRetinal diseases 1069ROCC[867]20173DOCTRetina72YesClsDiabetic Retinopathy 1070 OCT Glaucoma Detection[868] 20183DOCT Optic nerve head 1110YesClsGlaucoma 1071OLIVES[869]20222D/3DOCTEye1268YesClsDiabetic conditions 1072GAMMA[870]20212D/3DOCTRetina300YesCls/Seg/LocGlaucoma 1073 Farsiu Ophthalmology 2013[871] 20133DOCTRetina384YesSeg/Cls Age-related macular degeneration 1074Chiu IOVS 2011[872]20123DOCTRetina25YesSeg/Cls Macular Degenera- tion 1075Chiu BOE 2014 dataset[873]20143DOCTRetina16YesSeg Diabetic macular edema 1076Srinivasan BOE 2014[874]20143DOCTRetina45YesClsEye diseases 1077Soltanian Optica 2021[875]20213DOCTRetina8YesSegGlaucoma 1078STAGE[876]20233DOCTRetina400YesReg/ClsGlaucoma 1079Eye OCT Datasets[877]20213DOCTRetina148YesCls/SegRetinal diseases 1080OCTA-500[878]20243DOCT/OCTARetina500YesCls/SegRetinal diseases 1081OCTA2024[227]20243DOCT/OCTARetinaTBDYesRec/Trans OCT to OCTA translation Total:5,381+ C Tables of Medical Video Datasets Table 26: Video datasets. #DatasetYearDimModalityStructure Num of samples LabelTaskDiseases 1082CholecT50 [879]2023VideoEndoscopyGallbladder50YesCls, Det surgical instru- ment, action, target 1083CholecTriplet 2021 [880]2021VideoEndoscopyGallbladder45YesCls, Det surgical instru- ment, action, target 1084SurgVisDom [881]2020VideoEndoscopyBowel488YesClsskin lesion 1085CATARACTS [54]2017VideoMicroscopyRetina50YesCls, Det surgical work- flow 1086EndoVis 2018-SWAS [882]2018VideoEndoscopyColon42YesClssurgical phase 1087EndoVis 2019-SWSA[883]2019VideoEndoscopyGallbladder30YesCls surgical phase, action, instru- ment 1088 EndoVis 2020- CATARACTS Work- flow [54] 2020VideoMicroscopyRetina50YesCls surgical work- flow 1089 EndoVis 2020- MISAW [884] 2020VideoRGBArtificial vessel27YesClssurgical phase 1090 EndoVis 2021- PETRAW [885] 2021VideoEndoscopyNA150YesCls surgical work- flow 1091 EndoVis 2022- SurgToolLoc [886] 2022VideoEndoscopyNA24695YesCls surgical instru- ment 1092T3 Challenge [887]2023VideoRGBNA200YesCls, Det, VQA life-saving intervention procedure 1093Endo-FM[421]20232D, VideoEndoscopyNA32896NoNANA 1094OSS [888]2025VideoEndoscopyNA330YesCls surgical suturing skill 1095FedSurg [889]2024VideoEndoscopyNA30YesCls laparoscopic grading of the appendicitis 1096CardiacUDC[890]2023VideoUltrasoundHeart992YesSeg, Cls cardiac anatomi- cal structures 1097m2cai16-tool [402]2016VideoEndoscopyGallbladder15YesCls, Det surgical instru- ment 1098Cholec80 [891]2016VideoEndoscopyGallbladder80YesClssurgical phase 155 1099SAGES-CVS[892]2024VideoEndoscopy Gallbladder, Cystic Duct, Cystic Artery, Hepatocystic Triangle, Liver 18000YesClssurgical skill 1100CatRelDet[893]2020VideoMicroscopyRetina8YesCls, Detsurgical phase 1101SurgicalActions160[894]2017VideoEndoscopy Female Repro- ductive System 160YesCls, Retrievalsurgical phase 1102OphNet [895]2024VideoMicroscopyRetina743YesClssurgical phase 1103NurVid [896]2023VideoRGBNA1,538YesCls nursing proce- dure 1104m2cai16-workflow [897]2016VideoEndoscopyGallbladder41YesClssurgical phase 1105EndoCV 2021 [898]2021VideoEndoscopyPolyp4019YesDetsurgical actions 1106AdaptOR2021 [899]2021VideoEndoscopyHeart5584YesDet2D landmarks 1107CholecTriplet2022 [879]2022VideoEndoscopyGallbladder45YesCls, Det surgical instru- ment, action, target 1108 m2cai16-tool- locations [402] 2016VideoEndoscopyGallbladder15YesDet, Tracking surgical instru- ment 1109A-AFMA [900]2020VideoUltrasoundBladderNAYesDetamniotic fluid 1110GIANA [901]2017VideoEndoscopyColon3500YesSeg, Detangiodysplasia 1111 EndoVis 2021- SimSurgSkill [902] 2021VideoEndoscopyNA321YesDet, Cls surgical tool clevis and needle, surgical skill 1112AVOS[84]2024VideoEndoscopyNA1997YesDet, Tracking surgical instru- ment, action 1113 EndoVis 2022-SimCol-to- 3D[903] 2022VideoEndoscopyColon15YesEst depth, camera pose 1114KBD [79]2017VideoEndoscopyKidney4YesSegkidney boundary 1115EndoVis15-IST[904]20152D, VideoEndoscopyNA100YesSeg, Tracking surgical instru- ment 1116 Robotic Instrument Seg- mentation [905] 2017VideoEndoscopyBowel18YesSeg surgical instru- ment 1117ROBUST-MIS [906]2019VideoEndoscopyColon10YesSeg, Det surgical instru- ment 1118 EndoVis20- CATARACTS[907] 2020VideoMicroscopyRetina50YesSeg surgical instru- ment 1119EndoVis21-HeiSurf [908]2021VideoEndoscopyNA33Yes Seg, Det, Track- ing surgical phase, action, instru- ment, organ 1120EndoVis22-P2ILF [91]2022Video, 3D Endoscopy, CT Liver167YesSeg, Regliver 1121SegSTRONG-C [909]20242D, VideoEndoscopyNA17YesSeg Surgical instru- ment 1122SegCol [423]20242D, VideoEndoscopyNA78YesSeg surgical instru- ment, colon folds 1123FetReg [910]2021VideoEndoscopyPlacenta2060YesSegvessel 1124EndoVis23-PitVis [911]2024VideoEndoscopy Brain, Pituitary gland 25YesSeg, Det, Clssurgical phase 1125 EndoVis23- SurgToolLoc [912] 2023VideoEndoscopy Pituitary, Ab- domen 949YesSeg, Det, Cls surgical instru- ment 1126EndoVis18-RSS [913]2018VideoEndoscopyAbdomen15YesSeg, Tracking surgical instru- ment 1127 EndoVis22-SAR- RARP50 [914] 2022VideoEndoscopyProstate50YesSeg, Cls surgical instru- ment, action 1128PhaKIR [915]2024VideoEndoscopyGallbladder13YesSeg, Det, Cls surgical phase, instrument, instrument keypoint 1129SurgVU [90]2024VideoEndoscopyAbdomen155YesSeg, Det, Cls surgical phase, instrument 1130Cataract-1K [93]2023VideoMicroscopyRetina, Iris, Pupil2256YesSeg, Det, Cls surgical phase, instrument, abnormality 1131LensID [916]2021VideoMicroscopyRetina2589YesDet, Seg surgical phase, instrument, anatomy 1132AutoLaparo [917]2022VideoEndoscopyUterus21YesSeg surgical phase, action, instru- ment and key anatomy 1133CholecSeg8k [918]2020VideoEndoscopyGallbladder17YesSegsurgical elements 1134CholecInstanceSeg [919]2024VideoEndoscopyGallbladder85YesSeg surgical instru- ment 1135CaDIS [920]2019VideoMicroscopyRetina, Iris, Pupil25YesSeg surgical full scene 1136Endoscapes2023 [921]2023VideoEndoscopyGallbladder201YesCls, Det, Seg surgical anatomy, instrument, skill 1137 The Dresden Surgical Anatomy Dataset [922] 2023VideoEndoscopy Abdominal organs, vessel structures 32YesSegsurgical anatomy 1138PolypGen [923]2023VideoEndoscopyPolyp2,225YesSegpolyp 156 1139 ASU-Mayo polyp database [924] 2022VideoEndoscopyPolyp38YesSegpolyp 1140GynSurg [925]2025VideoEndoscopyUterus15YesCls, Seg surgical instru- ment, anatomy 1141SurgT: Surgical Track[926]2022videoEndoscopyNA137YesTrackingsoft tissue 1142STIR[927]2023VideoEndoscopyNA60YesTrackingtissue keypoint 1143HiSWA-RLLS[928]2024VideoEndoscopyLiver50YesDet, Cls surgical phase, action, instru- ment 1144Egosurgery[929]2024VideoEndoscopyNA27000YesCls, Det surgical phase, instrument 1145TN-SCUI2020 [930]2020VideoUltrasoundThyroid gland637YesClsthyroid nodules 1146EchoNet-Dynamic[931]2020VideoUltrasoundHeart10030YesMeasurementheart 1147Gastrointestinal Atlas[932]2000VideoEndoscopyBowel, Stomach5142NoNANA 1148Cataract-101[933]2018VideoMicroscopyRetina101YesClssurgical phase 1149MedVidQA[934]2022VideoRGBNA3010YesVQA visual question answering 1150HMC-QU[935]2021VideoUltrasoundHeart162YesCls myocardial infarction 1151Endovis 2019-SCRE[936]2019VideoEndoscopyPorcine cadaver9Yes Recon, Est, Stereo Matching depth 1152HyperKvasir[83]2020VideoEndoscopy Colon, Esopha- gus, Stomach 373YesSeg, Det, Clspolyp 1153ERS[937]2022VideoEndoscopy Gastrointestinal tract 1520YesCls, SegAbnormality 1154SUN-SEG[409]2022VideoEndoscopyColon1106YesSegpolyp 1155SARAS-MESAD [88]2021VideoEndoscopyProstate, Bladder4YesDetsurgical action 1156Ophora-160K [81]2025VideoMicroscopyRetina9819YesVideo generationVideo caption 1157POCUS [938]2020VideoUltrasoundLung64YesClsCOVID-19 1158CLUST [154]2014VideoUltrasoundLiver63YesTrackingNA Total:166,691 Abbreviations: Seg=Segmentation, Cls=Classification, Pred=Prediction, Det=Detection, Recon=Reconstruction, Reg=Registration, Est=Estimation, VQA=Visual Question Answering. 157