Summary of Mixed Prototype Consistency Learning For Semi-supervised Medical Image Segmentation, by Lijian Li
Mixed Prototype Consistency Learning for Semi-supervised Medical Image Segmentation
by Lijian Li
First submitted to arxiv on: 16 Apr 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 paper presents Mixed Prototype Consistency Learning (MPCL), a novel framework for semi-supervised medical image segmentation that leverages prototype learning to achieve remarkable performance. The MPCL framework combines a Mean Teacher and an auxiliary network to generate prototypes for labeled and unlabeled data, as well as mixed data processed by CutMix. By fusing these prototypes, the framework forms high-quality global prototypes for each class, optimizing the distribution of hidden embeddings used in consistency learning. Experimental results on left atrium and type B aortic dissection datasets demonstrate MPCL’s superiority over previous state-of-the-art approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using artificial intelligence to help doctors better understand medical images. The problem is that there isn’t enough labeled data, which limits how well the AI can do its job. To solve this problem, the researchers created a new way of doing things called Mixed Prototype Consistency Learning (MPCL). It’s like having multiple experts working together to come up with the best solution. They tested it on two different types of medical images and it worked really well. |
Keywords
» Artificial intelligence » Image segmentation » Semi supervised
