Acquiring pixel-level annotations is often limited in applications such as histology studies
that require domain expertise. Various semi-supervised learning approaches have been …

Semantic segmentation is a fundamental task in computer vision and finds extensive
applications in scene understanding, medical image analysis, and remote sensing. With the …

Deep learning has seen rapid growth in recent years and achieved state-of-the-art
performance in a wide range of applications. However, training models typically requires …

Convolutional neural networks (CNNs) have achieved significant success in medical image
segmentation. However, they also suffer from the requirement of a large number of …

The liver is one of the organs with the highest incidence rate in the human body, and late-
stage liver cancer is basically incurable. Therefore, early diagnosis and lesion location of …

Semi-supervised medical image segmentation has shown promise in training models with
limited labeled data. However, current dominant teacher-student based approaches can …

Collaborative learning is a fundamental component of consistency learning. It has been
extensively utilized in semi-supervised medical image segmentation, primarily based on the …

Medical image segmentation plays a crucial role in computer-aided diagnosis. However,
existing methods heavily rely on fully supervised training, which requires a large amount of …

The hybrid architecture of convolutional neural networks (CNNs) and Transformer are very
popular for medical image segmentation. However, it suffers from two challenges. First …

We present a novel semi-supervised framework for breast ultrasound (BUS) image
segmentation which is a very challenging task owing to (1) large scale and shape variations …