This repository serves as a curated collection of research papers and code related to the application of Transformer models in medical image analysis. It targets researchers and practitioners in medical imaging and computer vision, providing a comprehensive overview of state-of-the-art Transformer-based methods for tasks like segmentation, registration, classification, and synthesis.

How It Works

The repository organizes papers by task category (e.g., Image Segmentation, Image Registration, Image Classification & Detection, Denoising, Synthesis, Reconstruction). Each entry includes publication date, first author, title, modality (e.g., MRI, CT, X-ray), dimensionality (2D/3D), and availability of code or a link to the paper. It also includes foundational Transformer papers for context.

Quick Start & Requirements

This repository is a collection of papers and does not have a direct installation or execution command. Users will need to access the linked papers and potentially the associated code repositories for specific implementations.

Highlighted Details

• Comprehensive coverage of Transformer applications across various medical imaging tasks.
• Includes links to papers and, where available, code implementations (PyTorch, TensorFlow, JAX).
• Covers both 2D and 3D medical image analysis.
• Features foundational papers like "Attention Is All You Need" and "An Image is Worth 16x16 Words."

Maintenance & Community

The last update noted is March 17, 2022. No specific community links or active maintenance signals are provided in the README.

Licensing & Compatibility

The repository itself does not specify a license. Individual papers and code repositories will have their own licenses, which must be consulted for usage and compatibility.

Limitations & Caveats

The repository is a static collection of links and does not provide a unified framework or executable code. The information may not be exhaustive or up-to-date beyond the last recorded update date.