… This chapter describes the classic statistical machine learning context in which the domain
adaptation theory presented in this book stands. As mentioned in the Introduction, we focus …

… Recent advancements have shown that masked image … Thus, in medical imaging
problems, there is a strong need … Deep learning-based facial appearance simulation driven by …

… gained impressive advances in medical image segmentation … In deep learning, self-training
has received increasing … ), results of other unsupervised domain adaptation methods (4th-9th …

… advanced deep learning-based methods for medical image … in medical industry and
academia. This paper reviewed the recent progress of deep learning research in medical image …

… The accuracy and robustness of image classification with supervised deep learning are
dependent on the availability of large-scale, annotated training data. However, there is a paucity …

… deep learning models, a large number of algorithms for deep unsupervised domain adaptation
have emerged in recent … This section presents deep unsupervised domain adaptation of …

… latest developments and applications of unsupervised deep learning-based registration
methods in the medical … future study on medical image registration using deep learning, we have …

… of retinal imaging devices poses a significant challenge: domain shift, … deep learning models
to new testing domains. In this paper, we propose a novel unsupervised domain adaptation …

… Deep learning has been successfully applied in medical … To increase the robustness of
deep networks to domain shifts, … : domain generalization and unsupervised domain adaptation (…

… use in medical imaging, their utility in biological imaging is just being explored. Recent work
… ), which remains a critical biological imaging tool for neuroscientific discoveries that expand …