Summary of Log-vmamba: Local-global Vision Mamba For Medical Image Segmentation, by Trung Dinh Quoc Dang et al.
LoG-VMamba: Local-Global Vision Mamba for Medical Image Segmentation
by Trung Dinh Quoc Dang, Huy Hoang Nguyen, Aleksei Tiulpin
First submitted to arxiv on: 26 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| 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 (SSM), has recently shown competitive performance to Convolutional Neural Networks (CNNs) and Transformers in Natural Language Processing and general sequence modeling. The SSM’s ability to achieve global receptive fields, similar to Vision Transformers, while maintaining linear complexity in the number of tokens makes it particularly attractive for Computer Vision tasks, including medical image segmentation (MIS). However, existing Mamba-based networks struggle to maintain both spatially local and global dependencies of tokens in high-dimensional arrays due to their sequential nature. To address this limitation, we propose Local-Global Vision Mamba (LoG-VMamba), which explicitly enforces spatially adjacent tokens to remain nearby on the channel axis while retaining global context in a compressed form. Our method allows SSMs to access local and global contexts before reaching the last token, requiring only a simple scanning strategy. LoG-VMamba models are computationally efficient and substantially outperform CNN and Transformers-based baselines on diverse 2D and 3D MIS tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Mamba is a type of model that does well in certain tasks like language processing and general sequence modeling. It’s similar to other popular models like Convolutional Neural Networks (CNNs) and Transformers. Researchers have tried to use Mamba for computer vision tasks, like segmenting medical images. One problem with this approach is that it can be slow and difficult to do well on high-dimensional images. To fix this, scientists developed a new type of model called Local-Global Vision Mamba (LoG-VMamba). This new model does a better job at keeping track of both small and big patterns in the data while still being fast. It actually performs much better than other popular models like CNNs and Transformers on certain tasks. |
Keywords
» Artificial intelligence » Cnn » Image segmentation » Natural language processing » Token
