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

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

The hybrid architecture of CNNs and Transformers has gained popularity in medical image
segmentation. However, in this hybrid architecture, the semantic gaps between multi-scale …

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

Recently, transformers have been widely used in medical image segmentation to capture
long-range and global dependencies using self-attention. However, they often struggle to …

Image segmentation is an essential step in medical image analysis, as its results directly
affect the quality of the follow-up analysis. Because of the high calculation speed and the …

Automatic segmentation of medical images provides a reliable scientific basis for disease
diagnosis and analysis. Notably, most existing methods that combine the strengths of …

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

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

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 …