47 citations · 73 across the 9 of their papers we have counts for
10 papers
A deep Q-learning method for optimizing visual search strategies in backgrounds of dynamic noise
Weimin Zhou, Miguel P. Eckstein
Humans process visual information with varying resolution (foveated visual system) and explore images by orienting through eye movements the high-resolution fovea to points of inte…
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…
Approximating the Ideal Observer for joint signal detection and localization tasks by use of supervised learning methods
Weimin Zhou, Hua Li, Mark A. Anastasio
Medical imaging systems are commonly assessed and optimized by use of objective measures of image quality (IQ). The Ideal Observer (IO) performance has been advocated to provide a…
Learning stochastic object models from medical imaging measurements using Progressively-Growing AmbientGANs
Weimin Zhou, Sayantan Bhadra, Frank J. Brooks +2
It has been advocated that medical imaging systems and reconstruction algorithms should be assessed and optimized by use of objective measures of image quality that quantify the pe…
Learning Numerical Observers using Unsupervised Domain Adaptation
Shenghua He, Weimin Zhou, Hua Li +1
Medical imaging systems are commonly assessed by use of objective image quality measures. Supervised deep learning methods have been investigated to implement numerical observers f…