Curriculum learning (CL) is a training strategy that trains a machine learning model from
easier data to harder data, which imitates the meaningful learning order in human curricula …

Although deep learning models like CNNs have achieved great success in medical image
analysis, the small size of medical datasets remains a major bottleneck in this area. To …

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 …

Transfer learning from natural images to medical images has been established as one of the
most practical paradigms in deep learning for medical image analysis. To fit this paradigm …

Background and objectives The traditional method of detecting COVID-19 disease mainly
rely on the interpretation of computer tomography (CT) or X-ray images (X-ray) by doctors or …

Abstract Vision Transformer (ViT) has become one of the most popular neural architectures
due to its simplicity, scalability, and compelling performance in multiple vision tasks …

As one of the most ubiquitous diagnostic imaging tests in medical practice, chest
radiography requires timely reporting of potential findings and diagnosis of diseases in the …

We propose a novel knowledge distillation framework for effectively teaching a sensorimotor
student agent to drive from the supervision of a privileged teacher agent. Current distillation …

This paper considers the problem of multi-label thorax disease classification on chest X-ray
images. Identifying one or more pathologies from a chest X-ray image is often hindered by …

Abstract Background and Objective: For early breast cancer detection, regular screening
with mammography imaging is recommended. Routine examinations result in datasets with …