This repository serves as a curated list of recent papers on Medical Image Analysis presented at CVPR, targeting researchers and practitioners in the field. It aims to provide a centralized, up-to-date resource for the latest advancements, facilitating easier access to cutting-edge research in areas like image generation, segmentation, and foundation models for medical applications.

How It Works

The repository organizes papers by sub-fields within medical image analysis, such as Image Generation, Segmentation, Foundation Models, Vision-Language Models, and Computational Pathology. Each entry includes a title, a link to the paper, and often a link to the associated code, allowing users to quickly find and explore relevant research and its implementation.

Quick Start & Requirements

• Access to the papers and code via provided links.
• No installation or specific software requirements beyond a web browser and potentially code execution environments (e.g., Python, PyTorch) for the linked code repositories.
• Links to official quick-start guides, demos, or documentation are provided per paper where available.

Highlighted Details

• Comprehensive coverage of CVPR 2024 and 2025 medical imaging papers.
• Categorization across diverse medical imaging tasks including segmentation, generation, classification, and pathology.
• Direct links to both research papers and their corresponding code implementations.
• Inclusion of notable papers, including oral presentations and best paper candidates.

Maintenance & Community

The repository is maintained by MedAIerHHL and accepts contributions or corrections via email or pull requests. The last update was on 2025/04/09.

Licensing & Compatibility

The licensing of individual papers and code repositories varies and must be checked on a per-item basis. Compatibility for commercial use or closed-source linking depends entirely on the licenses of the linked resources.

Limitations & Caveats

This repository is a curated list and does not host any code or data itself. The quality and completeness of the linked resources are external to this project. Some papers may not have publicly available code.