Summary of Unsupervised Domain Adaptation For Brain Vessel Segmentation Through Transwarp Contrastive Learning, by Fengming Lin et al.
Unsupervised Domain Adaptation for Brain Vessel Segmentation through Transwarp Contrastive Learning
by Fengming Lin, Yan Xia, Michael MacRaild, Yash Deo, Haoran Dou, Qiongyao Liu, Kun Wu, Nishant Ravikumar, Alejandro F. Frangi
First submitted to arxiv on: 23 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 contrastive learning framework for unsupervised domain adaptation (UDA) aims to align the labelled source distribution with the unlabelled target distribution, which is crucial for medical image analysis. By narrowing the inter-domain gap between labelled 3DRA and unlabelled MRA modality data, the method improves vessel segmentation performance. This UDA approach is validated on cerebral vessel datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a simple yet powerful way to help computers learn from different types of images without being explicitly trained on those images. By using this technique, doctors can use computer programs to analyze medical images in ways that weren’t possible before. The method was tested on pictures of blood vessels in the brain and showed promise for improving how well these images are analyzed. |
Keywords
* Artificial intelligence * Domain adaptation * Unsupervised
