This paper reviewed the deep learning‐based studies for medical imaging synthesis and its
clinical application. Specifically, we summarized the recent developments of deep learning …

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 …

Imputation of missing images via source-to-target modality translation can improve diversity
in medical imaging protocols. A pervasive approach for synthesizing target images involves …

Generative adversarial networks have gained a lot of attention in the computer vision
community due to their capability of data generation without explicitly modelling the …

A brain Magnetic resonance imaging (MRI) scan of a single individual consists of several
slices across the 3D anatomical view. Therefore, manual segmentation of brain tumors from …

Magnetic resonance (MR) imaging plays an important role in clinical and brain exploration.
However, limited by factors such as imaging hardware, scanning time, and cost, it is …

Generative adversarial networks (GANs) are unsupervised deep learning approach in the
computer vision community which has gained significant attention from the last few years in …

Rationale and Objectives Generative adversarial networks (GANs) are deep learning
models aimed at generating fake realistic looking images. These novel models made a great …

Multi-contrast MRI acquisitions of an anatomy enrich the magnitude of information available
for diagnosis. Yet, excessive scan times associated with additional contrasts may be a …

Glioma is the most common primary intraparenchymal tumor of the brain and the 5-year
survival rate of high-grade glioma is poor. Magnetic resonance imaging (MRI) is essential for …