This repository serves as a comprehensive, curated collection of medical datasets, targeting researchers, clinicians, and data scientists. It aims to streamline the discovery and utilization of publicly available medical datasets, particularly for imaging and electronic health records, thereby accelerating advancements in healthcare research and patient care.

How It Works

The project functions as an extensive, categorized catalog of medical datasets. It organizes datasets by anatomical region (e.g., Head and Neck, Chest, Abdomen) and data modality (e.g., 2D X-ray, 3D CT, MRI, Ultrasound, Pathology). Each entry includes a description, official website link, release date, and any associated challenges, providing a structured overview for efficient dataset identification.

Quick Start & Requirements

This repository is a curated list and does not require installation or execution. Users can directly browse the categorized dataset links.

Highlighted Details

• Breadth of Coverage: Encompasses a vast array of medical imaging modalities and anatomical regions, with detailed metadata for each dataset.
• Challenge Integration: Links datasets to relevant challenges (e.g., MICCAI, ISBI, Kaggle), facilitating participation and benchmarking.
• Toolbox and SOTA: Includes links to foundational toolbox projects (e.g., nnU-Net, MONAI, SAM) and state-of-the-art methods used with these datasets.
• Community Driven: Welcomes contributions via issues or pull requests to expand the collection.

Maintenance & Community

The project is actively maintained, with the last update noted as January 23, 2025. It encourages community contributions, indicating an active user base.

Licensing & Compatibility

The repository itself is a collection of links and does not impose a specific license. Individual datasets will have their own licensing terms, which users must adhere to.

Limitations & Caveats

While comprehensive, the repository is a curated list; direct access or download of datasets is not provided, and users must navigate to the respective official websites. The quality and accessibility of linked datasets are dependent on their original sources.