99 citations · 386 across the 25 of their papers we have counts for
13 papers · 1 filter
On the impact of incorporating task-information in learning-based image denoising
Kaiyan Li, Hua Li, Mark A. Anastasio
A variety of deep neural network (DNN)-based image denoising methods have been proposed for use with medical images. These methods are typically trained by minimizing loss function…
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…
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…
Assessing the Impact of Deep Neural Network-based Image Denoising on Binary Signal Detection Tasks
Kaiyan Li, Weimin Zhou, Hua Li +1
A variety of deep neural network (DNN)-based image denoising methods have been proposed for use with medical images. Traditional measures of image quality (IQ) have been employed t…