The ability to generate synthetic medical images is useful for data augmentation, domain
transfer, and out-of-distribution detection. However, generating realistic, high-resolution …

Purpose To explore whether generative adversarial networks (GANs) can enable synthesis
of realistic medical images that are indiscernible from real images, even by domain experts …

Deep learning (DL) models are becoming pervasive and applicable to computer vision,
image processing, and synthesis problems. The performance of these models is often …

Deep Learning is vital in medical imaging solutions and clinical applications. However,
multiple reasons, such as data scarcity and imbalance in the medical image dataset, cause …

Abstract Background and Objective: In medical imaging, the scarcity of labeled lesion data
has hindered the application of many deep learning algorithms. To overcome this problem …

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 …

Currently generative adversarial networks (GANs) are rarely applied to medical images of
large sizes, especially 3D volumes, due to their large computational demand. We propose a …

Data scarcity and class imbalance are two fundamental challenges in many machine
learning applications to healthcare. Breast cancer classification in mammography …

In this research, we introduce an innovative method for synthesizing medical images using
generative adversarial networks (GANs). Our proposed GANs method demonstrates the …

Generative adversarial networks (GANs) are a class of unsupervised machine learning
algorithms that can produce realistic images from randomly-sampled vectors in a multi …