Summary of Robust Semi-supervised Multimodal Medical Image Segmentation Via Cross Modality Collaboration, by Xiaogen Zhou and Yiyou Sun and Min Deng and Winnie Chiu Wing Chu and Qi Dou
Robust Semi-supervised Multimodal Medical Image Segmentation via Cross Modality Collaboration
by Xiaogen Zhou, Yiyou Sun, Min Deng, Winnie Chiu Wing Chu, Qi Dou
First submitted to arxiv on: 14 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Image and Video Processing (eess.IV)
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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 semi-supervised multimodal segmentation framework is proposed for medical image segmentation, aiming to enhance performance by leveraging complementary information from different modalities while being robust to limited annotated data and anatomical misalignments. The framework employs a cross-modality collaboration strategy to distill modality-independent knowledge and integrate it into a unified fusion layer for feature amalgamation, along with channel-wise semantic consistency loss and contrastive consistent learning to regulate anatomical structures. This approach achieves competitive performance across three tasks: cardiac, abdominal multi-organ, and thyroid-associated orbitopathy segmentations, while demonstrating robustness in scenarios involving scarce labeled data and misaligned modalities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new way to combine information from different types of medical images to better identify different parts of the body. It’s hard for computers to do this because the different images are not perfectly aligned, but the new method helps them work together more effectively. The approach is tested on three different tasks and performs well even when there’s not much labeled data available. This could be useful in hospitals where they don’t have a lot of data to train their systems. |
Keywords
» Artificial intelligence » Image segmentation » Semi supervised
