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 …

Distribution shift widely exists in medical images acquired from different medical centres and
poses a significant obstacle to deploying the pre-trained semantic segmentation model in …

Real-world vision tasks frequently suffer from the appearance of unexpected adverse
weather conditions, including rain, haze, snow, and raindrops. In the last decade …

Recent advancements in artificial intelligence have witnessed human-level performance;
however, AI-enabled cognitive assistance for therapeutic procedures has not been fully …

Optical coherence tomography angiography (OCT-A) provides depth-resolved visualization
of the retinal microvasculature without intravenous dye injection. It facilitates investigations …

In medical image segmentation, domain generalization poses a significant challenge due to
domain shifts caused by variations in data acquisition devices and other factors. These shifts …

Deep learning (DL) based methods have been extensively studied for medical image
segmentation, mostly emphasizing the design and training of DL networks. Only few …

Continual test-time adaptation (CTTA) aims to continuously adapt a source-trained model to
a target domain with minimal performance loss while assuming no access to the source …