Accurate segmentation of lesions in medical images is a key step to assist clinicians in
diagnosis and analysis. Most studies combine the Transformer model with CNN at a single …

Accurate and automatic segmentation of medical images is a key step in clinical diagnosis
and analysis. Currently, the successful application of Transformers' model in the field of …

Automated medical image segmentation is a crucial step in clinical analysis and diagnosis,
as it can improve diagnostic efficiency and accuracy. Deep convolutional neural networks …

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

The Transformer network was originally proposed for natural language processing. Due to
its powerful representation ability for long-range dependency, it has been extended for …

Deep learning-based medical image segmentation has made great progress over the past
decades. Scholars have proposed many novel transformer-based segmentation networks to …

Medical image segmentation is crucial for accurate diagnosis and treatment in the medical
field. In recent years, convolutional neural networks (CNNs) and Transformers have been …

Transformer-based models have been widely demonstrated to be successful in computer
vision tasks by modeling long-range dependencies and capturing global representations …

Over the past few years, convolution neural networks (CNNs) and vision transformers (ViTs)
have been two dominant architectures in medical image segmentation. Although CNNs can …

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