Medical image segmentation is a fundamental research problem in the field of medical
image processing. Recently, the Transformer have achieved highly competitive performance …

Objective. Over the past years, convolutional neural networks based methods have
dominated the field of medical image segmentation. But the main drawback of these …

Integrating transformers and convolutional neural networks represents a crucial and cutting-
edge approach for tackling medical image segmentation problems. Nonetheless, the …

In recent years, Transformers have achieved success in the field of medical image
segmentation due to their outstanding capability to model long-range dependencies …

The UNet architecture, based on Convolutional Neural Networks (CNN), has demonstrated
its remarkable performance in medical image analysis. However, it faces challenges in …

Convolutional neural networks (CNNs) have been the de facto standard for nowadays 3D
medical image segmentation. The convolutional operations used in these networks …

Recently, the medical image segmentation have made rapid progress. Specifically, the
precision of medical image segmentation play a pivotal role in the realm of disease …

Medical image segmentation is essential in medical image analysis since it can provide
reliable assistance in computer-aided clinical diagnosis, treatment planning, and …

The success of Transformer in computer vision has attracted increasing attention in the
medical imaging community. Especially for medical image segmentation, many excellent …

Medical image segmentation faces current challenges in effectively extracting and fusing
long-distance and local semantic information, as well as mitigating or eliminating semantic …