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 …

Background and objective With the high-resolution (HR) requirements of medical images in
clinical practice, super-resolution (SR) reconstruction algorithms based on low-resolution …

Image-to-image translation is considered a new frontier in the field of medical image
analysis, with numerous potential applications. However, a large portion of recent …

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 …

Deep neural networks represent, nowadays, the most effective machine learning technology
in biomedical domain. In this domain, the different areas of interest concern the Omics (study …

Dermatology is the most appropriate field to utilize pattern recognition-based automated
techniques for objective, accurate, and rapid diagnosis because diagnosis mainly relies on …

Current deep neural network based approaches to computed tomography (CT) metal artifact
reduction (MAR) are supervised methods that rely on synthesized metal artifacts for training …

Sparse-view computed tomography (SVCT) aims to reconstruct a cross-sectional image
using a reduced number of x-ray projections. While SVCT can efficiently reduce the radiation …

Abstract Three‐dimensional (3D) image reconstruction of tumours can visualise their
structures with precision and high resolution. In this article, GAN‐LSTM‐3D method is …

Abstract Background and Objective Computer-aided-detection (CAD) systems have been
developed to assist radiologists on finding suspicious lesions in mammogram. Deep …