12 citations · 14 across the 3 of their papers we have counts for
3 papers
Assessing the ability of generative adversarial networks to learn canonical medical image statistics
Varun A. Kelkar, Dimitrios S. Gotsis, Frank J. Brooks +4
In recent years, generative adversarial networks (GANs) have gained tremendous popularity for potential applications in medical imaging, such as medical image synthesis, restoratio…
Evaluating Procedures for Establishing Generative Adversarial Network-based Stochastic Image Models in Medical Imaging
Varun A. Kelkar, Dimitrios S. Gotsis, Frank J. Brooks +4
Modern generative models, such as generative adversarial networks (GANs), hold tremendous promise for several areas of medical imaging, such as unconditional medical image synthesi…
Noise Entangled GAN For Low-Dose CT Simulation
Chuang Niu, Ge Wang, Pingkun Yan +8
We propose a Noise Entangled GAN (NE-GAN) for simulating low-dose computed tomography (CT) images from a higher dose CT image. First, we present two schemes to generate a clean CT…