In the past decade, the application of machine learning (ML) to healthcare has helped drive
the automation of physician tasks as well as enhancements in clinical capabilities and …

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 …

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 …

You are holding in your hands… oh, come on, who holds books like this in their hands
anymore? Anyway, you are reading this, and it means that I have managed to release one of …

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 …

Abstract Recently, deep learning (DL)‐based methods for the generation of synthetic
computed tomography (sCT) have received significant research attention as an alternative to …

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 …

The basic Generative Adversarial Networks (GAN) model is composed of the input vector,
generator, and discriminator. Among them, the generator and discriminator are implicit …

Artificial intelligence and machine learning (AI/ML) models have been adopted in a wide
range of healthcare applications, from medical image computing and analysis to continuous …