What has happened in machine learning lately, and what does it mean for the future of
medical image analysis? Machine learning has witnessed a tremendous amount of attention …

Medical image analysis is performed by analyzing images obtained by medical imaging
systems to solve clinical problems. The purpose is to extract effective information and …

We propose a novel attention gate (AG) model for medical image analysis that automatically
learns to focus on target structures of varying shapes and sizes. Models trained with AGs …

In a short period of time, many areas of science have made a sharp transition towards data-
dependent methods. In some cases, this process has been enabled by simultaneous …

COVID-19 epidemic has swiftly disrupted our day-to-day lives affecting the international
trade and movements. Wearing a face mask to protect one's face has become the new …

With the advancement in artificial intelligence (AI) and machine learning (ML) techniques,
researchers are striving towards employing these techniques for advancing clinical practice …

Brain vessel status is a promising biomarker for better prevention and treatment in
cerebrovascular disease. However, classic rule-based vessel segmentation algorithms need …

Alzheimer's disease (AD) is a degenerative neurological disease in elderly individuals.
Subjective cognitive decline (SCD), mild cognitive impairment (MCI) and further …

This article is a comprehensive review of the basic background, technique, and clinical
applications of artificial intelligence (AI) and radiomics in the field of neuro-oncology. A …

Abstract Machine learning (ML) is a branch of artificial intelligence centered on algorithms
which do not need explicit prior programming to function but automatically learn from …