Summary of Affinity-graph-guided Contractive Learning For Pretext-free Medical Image Segmentation with Minimal Annotation, by Zehua Cheng et al.
Affinity-Graph-Guided Contractive Learning for Pretext-Free Medical Image Segmentation with Minimal Annotation
by Zehua Cheng, Di Yuan, Thomas Lukasiewicz
First submitted to arxiv on: 14 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 proposed affinity-graph-guided semi-supervised contrastive learning framework (Semi-AGCL) combines semi-supervised learning (SemiSL) and contrastive learning (CL) to achieve medical image segmentation with minimal annotations. The framework establishes additional supervision signals between the student and teacher network, leveraging affinity graphs to improve representation quality and generalization ability. An average-patch-entropy-driven inter-patch sampling method provides a robust initial feature space without pretext tasks. The Semi-AGCL model approaches the accuracy of the fully annotated baseline with 10% annotations and surpasses the second best baseline by 23.09% on the CRAG and ACDC datasets with only 5% annotations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to help computers learn from medical images without needing lots of labels. The method combines two existing techniques: one that uses some labeled data, and another that compares similar things. They add an extra layer of information that helps the computer understand what it’s seeing. This allows the computer to get really good at recognizing different parts of a medical image even when only 10% of the image has been labeled. With just 5% labeling, this method does even better than other methods! |
Keywords
» Artificial intelligence » Generalization » Image segmentation » Semi supervised
