As an emerging biomedical image processing technology, medical image segmentation has
made great contributions to sustainable medical care. Now it has become an important …

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

Abstract Despite that Convolutional Neural Networks (CNNs) have achieved promising
performance in many medical image segmentation tasks, they rely on a large set of labeled …

In semi-supervised medical image segmentation, there exist empirical mismatch problems
between labeled and unlabeled data distribution. The knowledge learned from the labeled …

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

Deep learning-based semi-supervised learning (SSL) algorithms have led to promising
results in medical images segmentation and can alleviate doctors' expensive annotations by …

Automated segmentation in medical image analysis is a challenging task that requires a
large amount of manually labeled data. However, most existing learning-based approaches …

A key requirement for the success of supervised deep learning is a large labeled dataset-a
condition that is difficult to meet in medical image analysis. Self-supervised learning (SSL) …

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 …

The semantic image segmentation task consists of classifying each pixel of an image into an
instance, where each instance corresponds to a class. This task is a part of the concept of …