With the advent of machine learning (ML) and deep learning (DL) empowered applications
for critical applications like healthcare, the questions about liability, trust, and interpretability …

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 …

Recent years have seen a proliferation of research on adversarial machine learning.
Numerous papers demonstrate powerful algorithmic attacks against a wide variety of …

This is a condensed summary of an international multisociety statement on ethics of artificial
intelligence (AI) in radiology produced by the ACR, European Society of Radiology, RSNA …

Generative deep learning algorithms have progressed to a point where it is difficult to tell the
difference between what is real and what is fake. In 2018, it was discovered how easy it is to …

Despite significant improvements over the last few years, cloud-based healthcare
applications continue to suffer from poor adoption due to their limitations in meeting stringent …

Abstract Internet of Medical Things (IoMT) supports traditional healthcare systems by
providing enhanced scalability, efficiency, reliability, and accuracy of healthcare services. It …

Abstract Purpose Since Generative Adversarial Network (GAN) was introduced into the field
of deep learning in 2014, it has received extensive attention from academia and industry …

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 …

Cybersecurity has seen an increasing frequency and impact of cyberattacks and exposure of
Protected Health Information (PHI). The uptake of an Electronic Medical Record (EMR), the …