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Joint semi-supervised and contrastive learning enables zero-shot domain-adaptation and multi-domain segmentation
We introduce SegCLR, a versatile framework designed to segment volumetric images across different domains, employing supervised and contrastive learning simultaneously to effectively learn from both labeled and unlabeled data.
Unsupervised Domain Adaptation with Contrastive Learning for OCT Segmentation
We evaluate our methods for domain adaptation from a (labeled) source domain to an (unlabeled) target domain, each containing images acquired with different acquisition devices.
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