Summary of Polyp-ddpm: Diffusion-based Semantic Polyp Synthesis For Enhanced Segmentation, by Zolnamar Dorjsembe et al.
Polyp-DDPM: Diffusion-Based Semantic Polyp Synthesis for Enhanced Segmentation
by Zolnamar Dorjsembe, Hsing-Kuo Pao, Furen Xiao
First submitted to arxiv on: 6 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 study introduces Polyp-DDPM, a diffusion-based method for generating realistic images of polyps conditioned on masks. The approach addresses data limitations, high annotation costs, and privacy concerns associated with medical images. By conditioning the model on segmentation masks, Polyp-DDPM outperforms state-of-the-art methods in terms of image quality (FID score: 78.47) and segmentation performance (IoU: 0.7156). The method generates a synthetic dataset for training, enhancing polyp segmentation models to be comparable with real images. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Polyp-DDPM is a new way to make pictures of polyps that looks very realistic. It’s special because it uses masks to help the model learn what makes a polyp look like a polyp. This helps solve problems like having too little data, needing lots of people to label things, and keeping medical images private. The results are better than other methods in making both pictures (FID score: 78.47) and accurately identifying polyps (IoU: 0.7156). It also makes a big dataset for training that is just as good as real data. |
Keywords
* Artificial intelligence * Diffusion
