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 …

Deep learning is a branch of artificial intelligence where networks of simple interconnected
units are used to extract patterns from data in order to solve complex problems. Deep …

The core problem of Magnetic Resonance Imaging (MRI) is the trade off between
acceleration and image quality. Image reconstruction and super-resolution are two crucial …

In this paper, we present a semi-supervised deep learning approach to accurately recover
high-resolution (HR) CT images from low-resolution (LR) counterparts. Specifically, with the …

In recent years, significant progress has been made in developing more accurate and
efficient machine learning algorithms for segmentation of medical and natural images. In this …

Radiomics converts medical images into mineable data via a high-throughput extraction of
quantitative features used for clinical decision support. However, these radiomic features are …

Image super-resolution (SR) is one of the vital image processing methods that improve the
resolution of an image in the field of computer vision. In the last two decades, significant …

Magnetic resonance imaging (MRI) provides excellent soft-tissue contrast for clinical
diagnoses and research which underpin many recent breakthroughs in medicine and …

This article provides a focused overview of emerging technology in musculoskeletal MRI
and CT. These technological advances have primarily focused on decreasing examination …

Artificial intelligence (AI) shows tremendous promise in the field of medical imaging, with
recent breakthroughs applying deep‐learning models for data acquisition, classification …