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

Co‐training algorithm is one of the main methods of semi‐supervised learning in machine
learning, which explores the effective information in unlabeled data by multi‐learner …

Due to the difficulty in accessing a large amount of labeled data, semi-supervised learning is
becoming an attractive solution in medical image segmentation. To make use of unlabeled …

Deep learning has led to tremendous progress in the field of medical artificial intelligence.
However, training deep-learning models usually require large amounts of annotated data …

In semi-supervised medical image segmentation, most previous works draw on the common
assumption that higher entropy means higher uncertainty. In this paper, we investigate a …

Semi-supervised learning has a great potential in medical image segmentation tasks with a
few labeled data, but most of them only consider single-modal data. The excellent …

Semi-supervised learning (SSL) has recently demonstrated great success in medical image
segmentation, significantly enhancing data efficiency with limited annotations. However …

Automatic medical image segmentation plays a critical role in scientific research and
medical care. Existing high-performance deep learning methods typically rely on large …

Recent semi-supervised learning methods use pseudo supervision as core idea, especially
self-training methods that generate pseudo labels. However, pseudo labels are unreliable …

Semi-supervised learning reduces overfitting and facilitates medical image segmentation by
regularizing the learning of limited well-annotated data with the knowledge provided by a …