Paper 2603.27460
Project Imaging-X: A Survey of 1000+ Open-Access Medical Imaging Datasets for Foundation Model Development
- Published
- Mar 2026
- Research lab
- Independent
- Citations
- 2
- GitHub
- 472 stars
01 In brief
Summary
This survey reviews over 1,000 open-access medical imaging datasets released between 2000 and 2025, analyzing their modalities, tasks, anatomical regions, and limitations.
It finds the landscape is fragmented, small-scale, and unevenly distributed, with 2D images dominating, pathology and X-ray being the most common modalities, and classification and segmentation being the dominant tasks.
To address this fragmentation, the authors propose a Metadata-Driven Fusion Paradigm (MDFP) that standardizes metadata, aligns semantics, and creates fusion blueprints to integrate datasets into larger, more coherent resources.
They also release an interactive discovery portal and a unified table of datasets.
The survey identifies critical gaps, including the scarcity of multimodal datasets, restrictive licensing, and the lack of contextual intelligence, and concludes by advocating for broader data sharing, synthetic data generation, and annotation-efficient learning to advance medical foundation models.
02 From the paper
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.