Summary of Rethinking Information Loss in Medical Image Segmentation with Various-sized Targets, by Tianyi Liu et al.
Rethinking Information Loss in Medical Image Segmentation with Various-sized Targets
by Tianyi Liu, Zhaorui Tan, Kaizhu Huang, Haochuan Jiang
First submitted to arxiv on: 28 Mar 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 paper introduces a novel Stagger Network (SNet) for medical image segmentation, which effectively captures both local and global information to balance target detection across varying sizes. The SNet addresses the issue of information loss by introducing a Parallel Module that bridges semantic gaps between CNNs and ViTs, a Stagger Module that fuses semantically similar features, and an Information Recovery Module that recovers complementary information. Theoretical analysis shows that this approach leads to less information loss, and experimental results on the Synapse, ACDC, and MoNuSeg datasets demonstrate superior performance compared to recent SOTAs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Medical image segmentation is a challenge in computer vision. This paper proposes a new way to do it called Stagger Network (SNet). The SNet helps the model learn from both small and big things at the same time. It uses special modules to fix problems that can happen when using old models like CNNs and ViTs. The SNet is tested on three different datasets and performs better than other recent methods. |
Keywords
» Artificial intelligence » Image segmentation
