Deep learning has received extensive research interest in develo** new medical image
processing algorithms, and deep learning based models have been remarkably successful …

The medical imaging literature has witnessed remarkable progress in high-performing
segmentation models based on convolutional neural networks. Despite the new …

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 …

Semi-supervised learning has attracted much attention in medical image segmentation due
to challenges in acquiring pixel-wise image annotations, which is a crucial step for building …

Although deep learning models like CNNs have achieved great success in medical image
analysis, the small size of medical datasets remains a major bottleneck in this area. To …

Contrastive learning (CL) aims to learn useful representation without relying on expert
annotations in the context of medical image segmentation. Existing approaches mainly …

Unsupervised domain adaption (UDA), which aims to enhance the segmentation
performance of deep models on unlabeled data, has recently drawn much attention. In this …

Deep learning has an enormous impact on medical image analysis. Many computer-aided
diagnostic systems equipped with deep networks are rapidly reducing human intervention in …

In biomedical image analysis, information about the location and appearance of tumors and
lesions is indispensable to aid doctors in treating and identifying the severity of diseases …

Despite the remarkable performance of deep learning methods on various tasks, most
cutting-edge models rely heavily on large-scale annotated training examples, which are …