Rationale and Objectives Generative adversarial networks (GANs) are deep learning
models aimed at generating fake realistic looking images. These novel models made a great …

Artificial intelligence techniques involving the use of artificial neural networks—that is, deep
learning techniques—are expected to have a major effect on radiology. Some of the most …

Deep learning techniques have promoted the rise of artificial intelligence (AI) and performed
well in computer vision. Medical image analysis is an important application of deep learning …

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 advent of artificial intelligence (AI) and machine learning (ML) has revolutionized the
field of medicine. Although highly effective, the rapid expansion of this technology has …

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 …

In recent years, generative adversarial networks (GANs) have gained tremendous popularity
for various imaging related tasks such as artificial image generation to support AI training …

Although generative adversarial networks (GANs) have shown promise in medical imaging,
they have four main limitations that impede their utility: computational cost, data …

Artificial intelligence (AI) is expected to have a major effect on radiology as it demonstrated
remarkable progress in many clinical tasks, mostly regarding the detection, segmentation …

The identification of lesion within medical image data is necessary for diagnosis, treatment
and prognosis. Segmentation and classification approaches are mainly based on …