Accurate medical image segmentation is of great significance for subsequent diagnosis and
analysis. The acquisition of multi-scale information plays an important role in segmenting …

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 …

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 …

Accurate image segmentation plays a crucial role in medical image analysis, yet it faces
great challenges caused by various shapes, diverse sizes, and blurry boundaries. To …

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 …

While CNN-based methods have been the cornerstone of medical image segmentation due
to their promising performance and robustness, they suffer from limitations in capturing long …

Most recent semantic segmentation methods adopt a U-Net framework with an encoder-
decoder architecture. It is still challenging for U-Net with a simple skip connection scheme to …

Automatic medical image segmentation has achieved impressive results with the
development of Deep Learning. However, although convolutional neural network, especially …

Medical image segmentation plays an essential role in developing computer-assisted
diagnosis and treatment systems, yet it still faces numerous challenges. In the past few …

To alleviate the social contradiction between limited medical resources and increasing
medical needs, the medical image-assisted diagnosis based on deep learning has become …