Convolutional neural networks (CNNs) have been the consensus for medical image
segmentation tasks. However, they inevitably suffer from the limitation in modeling long …

Medical image segmentation-the prerequisite of numerous clinical needs-has been
significantly prospered by recent advances in convolutional neural networks (CNNs) …

We propose a novel transformer model, capable of segmenting medical images of varying
modalities. Challenges posed by the fine-grained nature of medical image analysis mean …

Abstract Vision Transformer (ViT) has emerged as a potential alternative to convolutional
neural networks for large datasets. However, applying ViT directly to medical image …

Transformers have shown great promise in medical image segmentation due to their ability
to capture long-range dependencies through self-attention. However, they lack the ability to …

Background: Automatic segmentation of medical images has progressed greatly owing to
the development of convolutional neural networks (CNNs). However, there are two …

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

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

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

Deep learning models represent the state of the art in medical image segmentation. Most of
these models are fully-convolutional networks (FCNs), namely each layer processes the …