Summary of Polyp-ses: Automatic Polyp Segmentation with Self-enriched Semantic Model, by Quang Vinh Nguyen et al.
Polyp-SES: Automatic Polyp Segmentation with Self-Enriched Semantic Model
by Quang Vinh Nguyen, Thanh Hoang Son Vo, Sae-Ryung Kang, Soo-Hyung Kim
First submitted to arxiv on: 2 Oct 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 proposes a novel approach to automatic polyp segmentation in colonoscopy images, leveraging deep learning techniques like CNNs and Transformers. The existing methods often lack contextual understanding of polyps due to limited feature representation and handling of variability in appearance. To address this, the authors introduce the “Automatic Polyp Segmentation with Self-Enriched Semantic Model”, which extracts features from input images, generates an initial segmentation mask, and uses a self-enriched semantic module to query and augment deep features for better contextual understanding. The proposed method outperforms state-of-the-art baselines on five polyp benchmarks in terms of superior learning and generalization capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using computers to help doctors find and diagnose problems in pictures taken during colonoscopies. It’s a big problem because the pictures can be tricky to understand, and doctors need accurate information to make good decisions. The authors are trying to improve this process by creating a new way for computers to look at these pictures and figure out what’s going on. They’re using special computer programs that are really good at learning and recognizing patterns. These programs will help the computers learn more about what the pictures mean, so doctors can get better information. |
Keywords
» Artificial intelligence » Deep learning » Generalization » Mask
