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

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 …

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 …

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 …

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

With the improvement of hardware computing power and the development of deep learning
algorithms, a revolution of" artificial intelligence (AI)+ medical image" is taking place …

Deep learning has been widely used for medical image segmentation and a large number of
papers has been presented recording the success of deep learning in the field. A …

Training machine learning models in a meaningful order, from the easy samples to the hard
ones, using curriculum learning can provide performance improvements over the standard …

In this study, we propose a three-dimensional Medical image classifier using Multi-plane
and Multi-slice Transformer (M3T) network to classify Alzheimer's disease (AD) in 3D MRI …

Currently, the automatic segmentation of breast tumors based on breast ultrasound (BUS)
images is still a challenging task. Most lesion segmentation methods are implemented …