Recent advances in deep learning methods have redefined the state-of-the-art for many
medical imaging applications, surpassing previous approaches and sometimes even …

Generalizing a deep learning model to new domains is crucial for computer-aided medical
diagnosis systems. Most existing unsupervised domain adaptation methods have made …

Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance for
medical image segmentation, yet need plenty of manual annotations for training. Semi …

Unsupervised domain adaptation (UDA) aims to transfer knowledge learned from a labeled
source domain to an unlabeled and unseen target domain, which is usually trained on data …

Unsupervised domain adaptation has increasingly gained interest in medical image
computing, aiming to tackle the performance degradation of deep neural networks when …

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 …

Although deep convolutional networks have reached state-of-the-art performance in many
medical image segmentation tasks, they have typically demonstrated poor generalisation …

Unsupervised domain adaptation (UDA) has been a vital protocol for migrating information
learned from a labeled source domain to facilitate the implementation in an unlabeled …

Abstract Domain adaptation (DA) has drawn high interests for its capacity to adapt a model
trained on labeled source data to perform well on unlabeled or weakly labeled target data …

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