3 citations · 5 across the 5 of their papers we have counts for
7 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…
Application of DatasetGAN in medical imaging: preliminary studies
Zong Fan, Varun Kelkar, Mark A. Anastasio +1
Generative adversarial networks (GANs) have been widely investigated for many potential applications in medical imaging. DatasetGAN is a recently proposed framework based on modern…
Prior image-based medical image reconstruction using a style-based generative adversarial network
Varun A. Kelkar, Mark A. Anastasio
Computed medical imaging systems require a computational reconstruction procedure for image formation. In order to recover a useful estimate of the object to-be-imaged when the rec…
Impact of deep learning-based image super-resolution on binary signal detection
Xiaohui Zhang, Varun A. Kelkar, Jason Granstedt +2
Deep learning-based image super-resolution (DL-SR) has shown great promise in medical imaging applications. To date, most of the proposed methods for DL-SR have only been assessed…
Prior Image-Constrained Reconstruction using Style-Based Generative Models
Varun A. Kelkar, Mark A. Anastasio
Obtaining a useful estimate of an object from highly incomplete imaging measurements remains a holy grail of imaging science. Deep learning methods have shown promise in learning o…