Summary of A Comprehensive Survey Of Mamba Architectures For Medical Image Analysis: Classification, Segmentation, Restoration and Beyond, by Shubhi Bansal et al.
A Comprehensive Survey of Mamba Architectures for Medical Image Analysis: Classification, Segmentation, Restoration and Beyond
by Shubhi Bansal, Sreeharish A, Madhava Prasath J, Manikandan S, Sreekanth Madisetty, Mohammad Zia Ur Rehman, Chandravardhan Singh Raghaw, Gaurav Duggal, Nagendra Kumar
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Mamba, a State Space Model-based approach, offers an alternative to template-based deep learning methods in medical image analysis. While transformers excel in specific domains, they have limitations, such as quadratic computational complexity and inefficient handling of long-range dependencies. This hinders the analysis of large datasets in medical imaging, where spatial and temporal relationships are crucial. In contrast, Mamba boasts linear time complexity, processing longer sequences without attention mechanisms, and demonstrating strong performance in merging multimodal data, improving diagnosis accuracy and patient outcomes. The paper defines core concepts, explores Mamba architectures, covers optimizations, and presents experimental results, highlighting the transformative potential of Mamba in medical imaging. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Mamba is a new way to analyze medical images that’s faster and better than other deep learning methods. It can handle complex relationships between different parts of an image, which is important for diagnosing diseases and making accurate predictions. Mamba is especially good at combining different types of data, like X-rays and MRIs, to get a more complete picture of what’s going on in the body. This makes it useful for improving diagnosis accuracy and patient outcomes. |
Keywords
* Artificial intelligence * Attention * Deep learning
