Automatic medical image segmentation is a crucial topic in the medical domain and
successively a critical counterpart in the computer-aided diagnosis paradigm. U-Net is the …

Following unprecedented success on the natural language tasks, Transformers have been
successfully applied to several computer vision problems, achieving state-of-the-art results …

Abstract Background and Objectives: Transformer is a model relying entirely on self-
attention which has a wide range of applications in the field of natural language processing …

Transformers have dominated the field of natural language processing and have recently
made an impact in the area of computer vision. In the field of medical image analysis …

Transformer, one of the latest technological advances of deep learning, has gained
prevalence in natural language processing or computer vision. Since medical imaging bear …

In action recognition, although the combination of spatio-temporal videos and skeleton
features can improve the recognition performance, a separate model and balancing feature …

Automated segmentation in medical image analysis is a challenging task that requires a
large amount of manually labeled data. However, most existing learning-based approaches …

Artificial Intelligence (AI) solutions have been widely used in healthcare, and recent
developments in deep neural networks have contributed to significant advances in medical …

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

Recently, the vision transformer and its variants have played an increasingly important role
in both monocular and multi-view human pose estimation. Considering image patches as …