Medical image segmentation is a fundamental and critical step in many image-guided
clinical approaches. Recent success of deep learning-based segmentation methods usually …

The crux of medical image segmentation stems from learning pixel-wise semantic
consistency from a mount of labeled samples through exhaustive annotation. Most existing …

Despite the success of deep neural networks in medical image classification, the problem
remains challenging as data annotation is time-consuming, and the class distribution is …

Semi-supervised learning (SSL) has demonstrated remarkable advances on medical image
classification, by harvesting beneficial knowledge from abundant unlabeled samples. The …

Medical image classification presents significant challenges due to limited labeled samples
and class imbalance resulting from varying disease prevalence. In many real-world …

Semi-supervised learning (SSL) for medical image classification has achieved exceptional
success on efficiently exploiting knowledge from unlabeled data with limited labeled data …

While supervised learning has achieved remarkable success, obtaining large-scale labeled
datasets in biomedical imaging is often impractical due to high costs and the time …

Computer-aided design (CAD) tools are increasingly popular in modern dental practice,
particularly for treatment planning or comprehensive prognosis evaluation. In particular, the …

Self-ensembling framework has proven to be a powerful paradigm for semi-supervised
medical image classification by leveraging abundant unlabeled data. However, the …

Objective. Automated medical image segmentation is vital for the prevention and treatment
of disease. However, medical data commonly exhibit class imbalance in practical …