107 citations · 182 across the 12 of their papers we have counts for
13 papers
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…
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…
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…
Advancing the AmbientGAN for learning stochastic object models
Weimin Zhou, Sayantan Bhadra, Frank J. Brooks +3
Medical imaging systems are commonly assessed and optimized by use of objective-measures of image quality (IQ) that quantify the performance of an observer at specific tasks. Varia…
Exploiting Diverse Characteristics and Adversarial Ambivalence for Domain Adaptive Segmentation
Bowen Cai, Huan Fu, Rongfei Jia +3
Adapting semantic segmentation models to new domains is an important but challenging problem. Recently enlightening progress has been made, but the performance of existing methods…