The combination of the U-Net based deep learning models and Transformer is a new trend
for medical image segmentation. U-Net can extract the detailed local semantic and texture …

Medical image segmentation is crucial for the development of computer-aided diagnostic
and therapeutic systems, but still faces numerous difficulties. In recent years, the commonly …

Automatic medical image segmentation has made great progress owing to powerful deep
representation learning. Inspired by the success of self-attention mechanism in transformer …

The hybrid architecture of convolution neural networks (CNN) and Transformer has been the
most popular method for medical image segmentation. However, the existing networks …

Deep learning architectures based on convolutional neural network (CNN) and Transformer
have achieved great success in medical image segmentation. Models based on the encoder …

In recent years, both convolutional neural networks (CNN) and transformers have
demonstrated impressive feature extraction capabilities in the field of medical image …

For the past several years, semantic segmentation method based on deep learning,
especially Unet, have achieved tremendous success in medical image processing. The U …

Deep neural networks are now widely used in the medical image segmentation field for their
performance superiority and no need of manual feature extraction. U-Net has been the …

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

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 …