Summary of Cross-model Mutual Learning For Exemplar-based Medical Image Segmentation, by Qing En et al.
Cross-model Mutual Learning for Exemplar-based Medical Image Segmentation
by Qing En, Yuhong Guo
First submitted to arxiv on: 18 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 A novel cross-model mutual learning framework for exemplar-based medical image segmentation is introduced, which leverages two models to mutually excavate implicit information from unlabeled data at multiple granularities. The proposed CMEMS (Cross-model Mutual learning framework for Exemplar-based Medical image Segmentation) eliminates confirmation bias and enables collaborative training to learn complementary information by enforcing consistency at different granularities across models. The approach involves cross-model image perturbation based mutual learning, which generates high-confidence pseudo-labels using weakly perturbed images to supervise predictions of strongly perturbed images across models. Additionally, cross-model multi-level feature perturbation based mutual learning is designed to broaden the perturbation space and enhance robustness. CMEMS is jointly trained using exemplar data, synthetic data, and unlabeled data in an end-to-end manner. The proposed method outperforms state-of-the-art segmentation methods with extremely limited supervision, demonstrating its effectiveness for medical image segmentation tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to help computers segment medical images is developed. It uses two models that work together to learn from lots of medical images without needing them all to be labeled. This helps make the process faster and easier. The method, called CMEMS, makes sure the two models agree with each other at different levels of detail. By doing this, it can learn new things and improve its performance. CMEMS is trained using a combination of labeled and unlabeled images, and it outperforms current methods that require more labeled data. |
Keywords
» Artificial intelligence » Image segmentation » Synthetic data
