The recent vision transformer (ie for image classification) learns non-local attentive
interaction of different patch tokens. However, prior arts miss learning the cross-scale …

Medical image segmentation is a crucial step in developing medical systems, especially for
assisting doctors in diagnosing and treating diseases. Currently, UNet has become the …

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

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

Transformer, born for long-range dependency establishment, has been widely studied as a
complementary of convolutional neural networks (CNNs) in medical image segmentation …

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

The CNN-based methods have achieved impressive results in medical image segmentation,
but they failed to capture the long-range dependencies due to the inherent locality of the …

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 …

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

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 …