Although generative adversarial networks (GANs) can produce large datasets, their limited
diversity and fidelity have been recently addressed by denoising diffusion probabilistic …

Generative adversarial networks (GANs) have become increasingly powerful, generating
mind-blowing photorealistic images that mimic the content of datasets they have been …

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 …

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

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 …

Generative adversarial networks (GANs) and their extensions have carved open many
exciting ways to tackle well known and challenging medical image analysis problems such …

In recent years, generative adversarial networks (GANs) have gained tremendous popularity
for potential applications in medical imaging, such as medical image synthesis, restoration …

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 …

Background: The emergence of generative adversarial networks (GANs) has provided new
technology and framework for the application of medical images. Specifically, a GAN …

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 …