Summary of Few-shot Medical Image Segmentation with High-fidelity Prototypes, by Song Tang et al.
Few-Shot Medical Image Segmentation with High-Fidelity Prototypes
by Song Tang, Shaxu Yan, Xiaozhi Qi, Jianxin Gao, Mao Ye, Jianwei Zhang, Xiatian Zhu
First submitted to arxiv on: 26 Jun 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 Detail Self-refined Prototype Network (DSPNet) aims to improve Few-shot Semantic Segmentation (FSS) in medical imaging scenarios by constructing high-fidelity prototypes that represent object foreground and background more comprehensively. The network models multi-modal structures using clustering and fuses channel-wise, while integrating channel-specific structural information under sparse regulation. This approach outperforms previous state-of-the-art methods on three challenging medical image benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DSPNet is a new way to help computers understand images by creating better “prototypes” of objects and backgrounds in medical pictures. Usually, it’s hard to teach computers about medical imaging with just one or two examples, but DSPNet can do this really well. It works by looking at the image in different ways and combining that information to create a more complete picture. |
Keywords
» Artificial intelligence » Clustering » Few shot » Multi modal » Semantic segmentation
