Introduction: There has been a rapid development of deep learning (DL) models for medical
imaging. However, DL requires a large labeled dataset for training the models. Getting large …

Deep learning has demonstrated remarkable performance across various tasks in medical
imaging. However, these approaches primarily focus on supervised learning, assuming that …

Medical image segmentation methods based on deep learning have made remarkable
progress. However, such existing methods are sensitive to data distribution. Therefore, slight …

Machine learning techniques used in computer-aided medical image analysis usually suffer
from the domain shift problem caused by different distributions between source/reference …

The success of deep learning has set new benchmarks for many medical image analysis
tasks. However, deep models often fail to generalize in the presence of distribution shifts …

Deep learning based medical image diagnosis has shown great potential in clinical
medicine. However, it often suffers two major difficulties in real-world applications: 1) only …

The success of automated medical image analysis depends on large-scale and expert-
annotated training sets. Unsupervised domain adaptation (UDA) has been raised as a …

Convolutional neural networks trained on publicly available medical imaging datasets
(source domain) rarely generalise to different scanners or acquisition protocols (target …

Despite the successes of deep neural networks on many challenging vision tasks, they often
fail to generalize to new test domains that are not distributed identically to the training data …

This paper presents a novel unsupervised domain adaptation framework, called Synergistic
Image and Feature Adaptation (SIFA), to effectively tackle the problem of domain shift …