Due to the Transformer's ability to capture long‐range dependencies through Self‐Attention,
it has shown immense potential in medical image segmentation. However, it lacks the …

The Transformer architecture has gained widespread acceptance in image segmentation.
However, it sacrifices local feature details and necessitates extensive data for training …

Accurate medical image segmentation is of great significance for computer aided diagnosis.
Although methods based on convolutional neural networks (CNNs) have achieved good …

Recent years have witnessed notable advancements in medical image segmentation
through deep convolutional neural networks. However, a notable limitation lies in the local …

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

Deep convolutional neural network (CNN) greatly promotes the automatic segmentation of
medical images. However, due to the inherent properties of convolution operations, CNN …

Convolutional neural networks (CNN), especially U-shaped networks, have become the
mainstream approach for medical image segmentation. However, due to the intrinsic locality …

In recent years, the Vision Transformer has gradually replaced the CNN as the mainstream
method in the field of medical image segmentation due to its powerful long-range …

Combining transformers and convolutional neural networks is considered one of the most
important directions for tackling medical image segmentation problems. To learn the long …

Medical image segmentation is a compelling fundamental problem and an important
auxiliary tool for clinical applications. Recently, the Transformer model has emerged as a …