Summary of Synergy-guided Regional Supervision Of Pseudo Labels For Semi-supervised Medical Image Segmentation, by Tao Wang et al.
Synergy-Guided Regional Supervision of Pseudo Labels for Semi-Supervised Medical Image Segmentation
by Tao Wang, Xinlin Zhang, Yuanbin Chen, Yuanbo Zhou, Longxuan Zhao, Tao Tan, Tong Tong
First submitted to arxiv on: 7 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel Synergy-Guided Regional Supervision of Pseudo Labels (SGRS-Net) framework is introduced for semi-supervised learning, which addresses noise contamination in pseudo labeling strategies. The framework builds upon mean teacher networks and incorporates a Mix Augmentation module to enhance unlabeled data. This is achieved by strategically partitioning pseudo labels into distinct regions based on synergy evaluation before and after augmentation. Additionally, the Region Loss Evaluation module assesses loss across each delineated area. Experimental results on the LA dataset demonstrate superior performance compared to state-of-the-art techniques, highlighting the efficiency and practicality of the proposed framework. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Semi-supervised learning is a way to use data that’s not labeled to help train models. This can make them better at handling real-world situations where data might be incomplete or biased. One popular method in this field is called pseudo labeling. But, existing methods often get fooled by noisy or incorrect data, which can ruin their performance. To fix this, researchers created a new framework that combines ideas from mean teacher networks and data augmentation techniques. This framework looks at how well different parts of the data work together before and after adding noise to it. It then uses this information to separate the good pseudo labels from the bad ones. The results show that this method performs better than other top methods on a dataset called LA. |
Keywords
» Artificial intelligence » Data augmentation » Semi supervised
