This repository is a curated collection of resources related to Mamba models, with a particular focus on their applications in medical image analysis. It serves as a comprehensive survey, providing links to research papers, code repositories, and discussions on various Mamba architectures and their adaptations for diverse tasks. The primary benefit is to offer a centralized hub for researchers and practitioners interested in leveraging State Space Models (SSMs) as efficient alternatives to Transformers, especially in domains requiring long-sequence modeling and computational efficiency.

How It Works

The collection is organized by application domain, including medical imaging, remote sensing, speech processing, and natural language processing. It highlights Mamba's core innovation: selective state spaces, which allow for linear-time sequence modeling by dynamically adapting to input data. This approach contrasts with the quadratic complexity of traditional Transformers, offering significant computational advantages for long sequences and complex data.

Quick Start & Requirements

This repository is a survey and does not have a direct installation or run command. It links to various Mamba-based projects, each with its own requirements. Users should refer to individual project READMEs for installation and prerequisites, which commonly include Python, deep learning frameworks (like PyTorch or TensorFlow), and potentially specific hardware (like GPUs) depending on the model's complexity.

Highlighted Details

• Comprehensive coverage of Mamba variants and applications across numerous domains.
• Emphasis on Mamba's efficiency and effectiveness as a Transformer alternative.
• Extensive links to research papers and associated code on GitHub.
• Specific focus on Mamba's utility in medical image analysis tasks like segmentation, reconstruction, and classification.

Maintenance & Community

The repository was first released on June 05, 2024, and is actively updated with new research. It cites a survey paper on arXiv and includes links to GitHub repositories for most listed projects, fostering community engagement and code sharing.

Licensing & Compatibility

The licensing information is not explicitly stated for the survey repository itself. However, the linked projects utilize various open-source licenses, predominantly permissive ones like MIT, allowing for broad compatibility and commercial use. Users should verify the license of each individual project they intend to use.

Limitations & Caveats

As a survey, this repository does not provide a unified framework or implementation. Users must navigate individual project repositories to access and utilize the Mamba models. The rapid evolution of Mamba research means the field is constantly expanding, and this collection may require continuous updates to remain exhaustive.