Medical image synthesis represents a critical area of research in clinical decision-making,
aiming to overcome the challenges associated with acquiring multiple image modalities for …

The goals of this review paper on deep learning (DL) in medical imaging and radiation
therapy are to (a) summarize what has been achieved to date;(b) identify common and …

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 models with convolutional neural network (CNN) backbones have
recently been established as state-of-the-art in numerous medical image synthesis tasks …

The coronavirus disease (COVID-19) is rapidly spreading all over the world, and has
infected more than 1,436,000 people in more than 200 countries and territories as of April 9 …

The tremendous success of machine learning algorithms at image recognition tasks in
recent years intersects with a time of dramatically increased use of electronic medical …

Deep learning algorithms, in particular convolutional networks, have rapidly become a
methodology of choice for analyzing medical images. This paper reviews the major deep …

Medical imaging plays a critical role in various clinical applications. However, due to
multiple considerations such as cost and radiation dose, the acquisition of certain image …

MR-only radiotherapy treatment planning requires accurate MR-to-CT synthesis. Current
deep learning methods for MR-to-CT synthesis depend on pairwise aligned MR and CT …

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 …