Deep learning has achieved widespread success in medical image analysis, leading to an
increasing demand for large-scale expert-annotated medical image datasets. Yet, the high …

Deep learning has achieved widespread success in medical image analysis, leading to an
increasing demand for large-scale expert-annotated medical image datasets. Yet, the high …

We introduce a new problem in unsupervised domain adaptation, termed as Generalized
Universal Domain Adaptation (GUDA), which aims to achieve precise prediction of all target …

This paper proposes a new active learning method for semantic segmentation. The core of
our method lies in a new annotation query design. It samples informative local image …

Abstract Domain adaptation adapts models to various scenes with different appearances. In
this field active domain adaptation is crucial in effectively sampling a limited number of data …

Abstract Continual Test-Time Adaptation (CTTA) involves adapting a pre-trained source
model to continually changing unsupervised target domains. In this paper, we systematically …

Abstract Domain adaptation (DA) is commonly employed in diabetic retinopathy (DR)
grading using unannotated fundus images, allowing knowledge transfer from labeled color …

Unsupervised domain adaptation (UDA) for image classification has made remarkable
progress in transferring classification knowledge from a labeled source domain to an …

Abstract Active Domain Adaptation (ADA) queries the labels of a small number of selected
target samples to help adapting a model from a source domain to a target domain. The local …

Despite the significant progress in unsupervised domain adaptation (UDA), the performance
of UDA methods is still far inferior to that of the fully supervised ones. In practical scenarios, it …