Summary of Multi-scale Information Sharing and Selection Network with Boundary Attention For Polyp Segmentation, by Xiaolu Kang et al.
Multi-scale Information Sharing and Selection Network with Boundary Attention for Polyp Segmentation
by Xiaolu Kang, Zhuoqi Ma, Kang Liu, Yunan Li, Qiguang Miao
First submitted to arxiv on: 18 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 The proposed Multi-scale information sharing and selection network (MISNet) is a novel approach for polyp segmentation in colonoscopy images. The paper addresses two key challenges: varying lighting conditions and indistinct boundaries between polyps and surrounding tissue. To overcome these issues, MISNet employs three key modules: the Selectively Shared Fusion Module (SSFM), Parallel Attention Module (PAM), and Balancing Weight Module (BWM). These modules enable effective information sharing and selection between low-level and high-level features, while also enhancing attention to boundaries and facilitating continuous refinement of boundary segmentation. Experimental results on five polyp segmentation datasets demonstrate that MISNet outperforms state-of-the-art methods in terms of accuracy and clarity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to identify polyps in colonoscopy images. This is important for diagnosing and treating colon cancer. The method uses special computer algorithms to help the model see more clearly and make better decisions. The approach is designed to handle different lighting conditions and the varying shapes and sizes of polyps. It’s like having a superpower that helps doctors find polyps more accurately. |
Keywords
» Artificial intelligence » Attention
