Abstract Machine learning methods strive to acquire a robust model during the training
process that can effectively generalize to test samples, even in the presence of distribution …

Unsupervised domain adaptation (UDA) via deep learning has attracted appealing attention
for tackling domain-shift problems caused by distribution discrepancy across different …

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

This paper studies source-free domain adaptive fundus image segmentation which aims to
adapt a pretrained fundus segmentation model to a target domain using unlabeled images …

In the domain adaptation problem, source data may be unavailable to the target client side
due to privacy or intellectual property issues. Source-free unsupervised domain adaptation …

The domain discrepancy existed between medical images acquired in different situations
renders a major hurdle in deploying pre-trained medical image segmentation models for …

Unsupervised domain adaptation (UDA) has increasingly gained interests for its capacity to
transfer the knowledge learned from a labeled source domain to an unlabeled target …

Computer aided diagnosis (CAD) systems based on ECG signals have become
indispensable tools in the automatic detection of Arrhythmia, significantly reducing human …

Unsupervised domain adaptation (UDA), which is used to alleviate the domain shift between
the source domain and target domain, has attracted substantial research interest. Previous …

Accurate polyp segmentation plays a critical role from colonoscopy images in the diagnosis
and treatment of colorectal cancer. While deep learning-based polyp segmentation models …