Since its renaissance, deep learning has been widely used in various medical imaging tasks
and has achieved remarkable success in many medical imaging applications, thereby …

Abstract Vision Transformers (ViTs), with the magnificent potential to unravel the information
contained within images, have evolved as one of the most contemporary and dominant …

Despite the considerable progress in automatic abdominal multi-organ segmentation from
CT/MRI scans in recent years, a comprehensive evaluation of the models' capabilities is …

With the unprecedented developments in deep learning, automatic segmentation of main
abdominal organs seems to be a solved problem as state-of-the-art (SOTA) methods have …

The rapid advancements in machine learning, graphics processing technologies and the
availability of medical imaging data have led to a rapid increase in the use of deep learning …

Deep learning has revolutionized image processing and achieved the-state-of-art
performance in many medical image segmentation tasks. Many deep learning-based …

Every year, millions of brain MRI scans are acquired in hospitals, which is a figure
considerably larger than the size of any research dataset. Therefore, the ability to analyze …

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 …

Manual image segmentation is a time-consuming task routinely performed in radiotherapy to
identify each patient's targets and anatomical structures. The efficacy and safety of the …

Shortage of fully annotated datasets has been a limiting factor in developing deep learning
based image segmentation algorithms and the problem becomes more pronounced in multi …