Deep learning based image segmentation has achieved the state-of-the-art performance in
many medical applications such as lesion quantification, organ detection, etc. However …

Recently, deep learning with Convolutional Neural Networks (CNNs) and Transformers has
shown encouraging results in fully supervised medical image segmentation. However, it is …

Semi-supervised medical image segmentation presents a compelling approach to
streamline large-scale image analysis, alleviating annotation burdens while maintaining …

Consistency learning plays a crucial role in semi-supervised medical image segmentation
as it enables the effective utilization of limited annotated data while leveraging the …

The success of deep learning methods in medical image segmentation tasks usually
requires a large amount of labeled data. However, obtaining reliable annotations is …

Exploiting unlabeled and labeled data augmentations has become considerably important
for semi-supervised medical image segmentation tasks. However, existing data …

The performance of supervised deep learning methods for medical image segmentation is
often limited by the scarcity of labeled data. As a promising research direction, semi …

Semi-supervised segmentation presents a promising approach for large-scale medical
image analysis, effectively reducing annotation burdens while achieving comparable …

Semi-supervised learning has demonstrated great potential in medical image segmentation
by utilizing knowledge from unlabeled data. However, most existing approaches do not …

By fully utilizing unlabeled data, the semi-supervised learning (SSL) technique has recently
produced promising results in the segmentation of medical images. Pseudo labeling and …