As an important building block in automatic medical systems, image segmentation has made
great progress due to the data-driving mechanism of deep architecture. Recently, numerous …

The resolution of feature maps is a critical factor for accurate medical image segmentation.
Most of the existing Transformer-based networks for medical image segmentation adopt a U …

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 …

Medical image segmentation has attracted increasing attention due to its practical clinical
requirements. However, the prevalence of small targets still poses great challenges for …

Medical image segmentation of tissue abnormalities, key organs, or blood vascular system
is of great significance for any computerized diagnostic system. However, automatic …

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 …

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

Automatic segmentation of medical images based on multi-modality is an important topic for
disease diagnosis. Although the convolutional neural network (CNN) has been proven to …

Transformers have been extensively studied in medical image segmentation to build
pairwise long-range dependence. Yet, relatively limited well-annotated medical image data …

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 …