Summary of Domain Generalization For Endoscopic Image Segmentation by Disentangling Style-content Information and Superpixel Consistency, By Mansoor Ali Teevno et al.
Domain Generalization for Endoscopic Image Segmentation by Disentangling Style-Content Information and SuperPixel Consistency
by Mansoor Ali Teevno, Rafael Martinez-Garcia-Pena, Gilberto Ochoa-Ruiz, Sharib Ali
First submitted to arxiv on: 19 Sep 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 This AI research paper proposes an approach for style-content disentanglement using instance normalization and instance selective whitening (ISW) to improve domain generalization in gastrointestinal cancer precursor detection. The method, called SUPRA, was previously used but lacked structural information, making it suboptimal for segmentation tasks. By combining ISW with SUPRA, the authors aim to enhance performance on polyp and Barrett’s Esophagus datasets. Evaluation results show notable enhancements over baseline and state-of-the-art methods, with improvements of 14%, 10%, 8%, and 18% on the polyp dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, researchers developed a new way to detect gastrointestinal cancer precursors more accurately. They used special imaging techniques like white-light imaging and fluorescence imaging, which are usually used together. However, most machine learning models don’t work well when trained on one type of image and tested on another. To fix this problem, the authors combined two earlier methods: SUPRA and ISW. This new approach allows machines to learn more about different types of images and improve their ability to detect polyps and other cancer precursors. |
Keywords
» Artificial intelligence » Domain generalization » Machine learning
