… the feature level of a deep neural network. Thus, we consider … of unsupervised domain
adaptation for medical image seg… Recent advances adopt adversarial learning to address do…

… been substantially improved with advances in deep learning approaches. The performance
… -quality labeled data in the new target domain. Unsupervised domain adaptation (UDA) has …

… barriers to deploying machine learning techniques in practice. To … a novel approach to
unsupervised domain adaptation. We … , “Visual domain adaptation: A survey of recent advances,” …

… medical images, accurate segmentation remains a major challenge. In this paper, we propose
an unsupervised domain adaptation … Deep learning based automatic segmentation is a …

… fail in the target domain due to the domain shift. Unsupervised domain adaptation aims to …
robust models trained on medical images from source domains to a new target domain. In this …

… unsupervised domain adaptation method for small bowel segmentation based on feature
disentanglement. To make the domain adaptation … architecture, and selectively adapt the non-…

… , to more recent approaches based on machine learning. The latter … This is a common problem
in supervised learning, which can … [46] segments medical images using a combination of …

… of unsupervised domain adaptation for image classification task in two tracks: traditional
methods and deep learning-based … network for medical image domain adaptation using mutual …

… Although great breakthroughs have been achieved for medical imaging, the … SOTA
unsupervised domain adaptation (UDA) methods on two cross-modality medical image datasets, …

… labeled source domain to a target domain without accessing … deep learning studies on
unsupervised domain adaptation … deep learning studies on source-free unsupervised domain …