This paper presents a review of deep learning (DL)-based medical image registration
methods. We summarized the latest developments and applications of DL-based registration …

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 …

Deep learning has revolutionized image processing and achieved the-state-of-art
performance in many medical image segmentation tasks. Many deep learning-based …

With the advent of advancements in deep learning approaches, such as deep convolution
neural network, residual neural network, adversarial network; U-Net architectures are most …

Abstract Accurate diagnosis of Autism Spectrum Disorder (ASD) followed by effective
rehabilitation is essential for the management of this disorder. Artificial intelligence (AI) …

A generative adversarial network (GAN) is one of the most significant research directions in
the field of artificial intelligence, and its superior data generation capability has garnered …

Purpose Current clinical application of cone‐beam CT (CBCT) is limited to patient setup.
Imaging artifacts and Hounsfield unit (HU) inaccuracy make the process of CBCT‐based …

Artificial intelligence (AI) is the field of computer science that aims to build smart devices
performing tasks that currently require human intelligence. Through machine learning (ML) …

Background Manual contouring is very labor‐intensive, time‐consuming, and subject to intra‐
and inter‐observer variability. An automated deep learning approach to fast and accurate …

Recent advancements with deep generative models have proven significant potential in the
task of image synthesis, detection, segmentation, and classification. Segmenting the medical …