Recent advances in deep learning-based medical image segmentation studies achieve
nearly human-level performance in fully supervised manner. However, acquiring pixel-level …

The success of deep convolutional neural networks (DCNNs) benefits from high volumes of
annotated data. However, annotating medical images is laborious, expensive, and requires …

Unsupervised domain adaptation (UDA) methods have shown their promising performance
in the cross-modality medical image segmentation tasks. These typical methods usually …

Unsupervised domain adaption (UDA), which aims to enhance the segmentation
performance of deep models on unlabeled data, has recently drawn much attention. In this …

While deep learning methods hitherto have achieved considerable success in medical
image segmentation, they are still hampered by two limitations:(i) reliance on large-scale …

Deep learning models trained on medical images from a source domain (eg eg imaging
modality) often fail when deployed on images from a different target domain, despite …

Unsupervised domain adaptation (UDA) for cross-modality medical image segmentation has
shown great progress by domain-invariant feature learning or image appearance …

Deep convolutional neural networks (CNNs) have achieved impressive performance in
medical image segmentation; however, their performance could degrade significantly when …

Medical image segmentation methods normally perform poorly when there is a domain shift
between training and testing data. Unsupervised Domain Adaptation (UDA) addresses the …

Abstract Domain shift, a phenomenon when there exists distribution discrepancy between
training dataset (source domain) and test dataset (target domain), is very common in …