Unsupervised Domain Adaptation for Brain Vessel Segmentation Through Transwarp Contrastive Learning
Lin F, Xia Y, Deo Y, Macraild M, Dou H, Liu Q, Wu K, Ravikumar N, Frangi AF (2024)
Publication Type: Conference contribution
Publication year: 2024
Journal
Publisher: IEEE Computer Society
Conference Proceedings Title: Proceedings - International Symposium on Biomedical Imaging
Event location: Athens, GRC
ISBN: 9798350313338
DOI: 10.1109/ISBI56570.2024.10635148
Abstract
Unsupervised domain adaptation (UDA) aims to align the labelled source distribution with the unlabelled target distribution to obtain domain-invariant predictive models. Since cross-modality medical data exhibit significant intra and interdomain shifts and most are unlabelled, UDA is more important while challenging in medical image analysis. This paper proposes a simple yet potent contrastive learning frame-work for UDA to narrow the inter-domain gap between labelled source and unlabelled target distribution. Our method is validated on cerebral vessel datasets. Experimental results show that our approach can learn latent features from labelled 3DRA modality data and improve vessel segmentation performance in unlabelled MRA modality data.
Involved external institutions
United Kingdom (GB)How to cite
APA:
Lin, F., Xia, Y., Deo, Y., Macraild, M., Dou, H., Liu, Q.,... Frangi, A.F. (2024). Unsupervised Domain Adaptation for Brain Vessel Segmentation Through Transwarp Contrastive Learning. In Proceedings - International Symposium on Biomedical Imaging. Athens, GRC: IEEE Computer Society.
MLA:
Lin, Fengming, et al. "Unsupervised Domain Adaptation for Brain Vessel Segmentation Through Transwarp Contrastive Learning." Proceedings of the 21st IEEE International Symposium on Biomedical Imaging, ISBI 2024, Athens, GRC IEEE Computer Society, 2024.
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