Summary of Ac-mambaseg: An Adaptive Convolution and Mamba-based Architecture For Enhanced Skin Lesion Segmentation, by Viet-thanh Nguyen et al.
AC-MAMBASEG: An adaptive convolution and Mamba-based architecture for enhanced skin lesion segmentation
by Viet-Thanh Nguyen, Van-Truong Pham, Thi-Thao Tran
First submitted to arxiv on: 5 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper proposes a novel model, AC-MambaSeg, for skin lesion segmentation in medical images. The model combines the Vision Mamba framework with advanced components such as Convolutional Block Attention Module (CBAM), Attention Gate, and Selective Kernel Bottleneck to enhance its ability to focus on informative regions and suppress background noise. AC-MambaSeg is evaluated on diverse datasets of skin lesion images, including ISIC-2018 and PH2, and outperforms existing segmentation methods. The proposed model has promising potential for improving computer-aided diagnosis systems and facilitating early detection and treatment of dermatological diseases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Skin lesion segmentation is important for diagnosing and treating skin diseases. This paper creates a new model to help doctors identify skin lesions from medical images. The model uses special parts, like attention modules, to focus on the right areas and ignore background noise. It’s tested on different types of skin lesion images and works better than other models. This could help doctors find skin problems earlier and treat them more effectively. |
Keywords
» Artificial intelligence » Attention
