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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.
Quantum Vision Transformers
In this work, quantum transformers are designed and analysed in detail by extending the state-of-the-art classical transformer neural network architectures known to be very performant in natural language processing and image analysis.
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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