Knee osteoarthritis is a prevalent disease worldwide. The automatic segmentation of knee
tissues in magnetic resonance (MR) images has important clinical utility in assessing knee …

In an effort to further advance semi-supervised generative and classification tasks, we
propose a simple yet effective training strategy called* dual pseudo training*(DPT), built …

Unsupervised domain adaptation (UDA) aims to learn robust classifiers for the target domain
by leveraging knowledge from annotated source domain. Existing methods concentrated on …

With the growing prevalence of unstructured and noisy data in real-world applications,
develo** noise-robust semi-supervised clustering methods has become increasingly …

Universal domain adaptation (UniDA) is the unsupervised domain adaptation with label
shift. UniDA aims to classify unlabeled target samples into one of the" known" categories or …

Objective. Deep learning algorithms have demonstrated impressive performance by
leveraging large labeled data. However, acquiring pixel-level annotations for medical image …

Semi-Supervised Learning (SSL) seeks to leverage large amounts of non-annotated data
along with the smallest amount possible of annotated data in order to achieve the same …