47 citations · 101 across the 20 of their papers we have counts for
20 papers
Score-Based Ideal Observer Approximation via Denoising Score Matching for Signal-Known-Exactly Detection Tasks
Weimin Zhou
The Bayesian Ideal Observer (IO) establishes the theoretical upper bound on task performance for binary detection tasks. However, analytical computation of the IO test statistic is…
Constructing efficient channels for ideal observers using the conjugate gradient method
Weimin Zhou
Task-based assessment of image quality (IQ) is critically important for the design and optimization of medical imaging systems. Ideal observers, including the Bayesian Ideal Observ…
Unsupervised Generation of Pseudo Normal PET from MRI with Diffusion Model for Epileptic Focus Localization
Wentao Chen, Jiwei Li, Xichen Xu +6
[F]fluorodeoxyglucose (FDG) positron emission tomography (PET) has emerged as a crucial tool in identifying the epileptic focus, especially in cases where magnetic resonance…
Ambient-Pix2PixGAN for Translating Medical Images from Noisy Data
Wentao Chen, Xichen Xu, Jie Luo +1
Image-to-image translation is a common task in computer vision and has been rapidly increasing the impact on the field of medical imaging. Deep learning-based methods that employ c…
AmbientCycleGAN for Establishing Interpretable Stochastic Object Models Based on Mathematical Phantoms and Medical Imaging Measurements
Xichen Xu, Wentao Chen, Weimin Zhou
Medical imaging systems that are designed for producing diagnostically informative images should be objectively assessed via task-based measures of image quality (IQ). Ideally, com…
Ideal Observer Computation by Use of Markov-Chain Monte Carlo with Generative Adversarial Networks
Weimin Zhou, Umberto Villa, Mark A. Anastasio
Medical imaging systems are often evaluated and optimized via objective, or task-specific, measures of image quality (IQ) that quantify the performance of an observer on a specific…
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…
A Hybrid Approach for Approximating the Ideal Observer for Joint Signal Detection and Estimation Tasks by Use of Supervised Learning and Markov-Chain Monte Carlo Methods
Kaiyan Li, Weimin Zhou, Hua Li +1
The ideal observer (IO) sets an upper performance limit among all observers and has been advocated for assessing and optimizing imaging systems. For general joint detection and est…
Learning stochastic object models from medical imaging measurements by use of advanced ambient generative adversarial networks
Weimin Zhou, Sayantan Bhadra, Frank J. Brooks +2
Purpose: To objectively assess new medical imaging technologies via computer-simulations, it is important to account for the variability in the ensemble of objects to be imaged. Th…
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…
A signal detection model for quantifying over-regularization in non-linear image reconstruction
Emil Y. Sidky, John Paul Phillips, Weimin Zhou +5
Purpose: Many useful image quality metrics for evaluating linear image reconstruction techniques do not apply to or are difficult to interpret for non-linear image reconstruction.…
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…
Approximating the Hotelling Observer with Autoencoder-Learned Efficient Channels for Binary Signal Detection Tasks
Jason L. Granstedt, Weimin Zhou, Mark A. Anastasio
The objective assessment of image quality (IQ) has been advocated for the analysis and optimization of medical imaging systems. One method of obtaining such IQ metrics is through a…
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…
Medical image reconstruction with image-adaptive priors learned by use of generative adversarial networks
Sayantan Bhadra, Weimin Zhou, Mark A. Anastasio
Medical image reconstruction is typically an ill-posed inverse problem. In order to address such ill-posed problems, the prior distribution of the sought after object property is u…
Markov-Chain Monte Carlo Approximation of the Ideal Observer using Generative Adversarial Networks
Weimin Zhou, Mark A. Anastasio
The Ideal Observer (IO) performance has been advocated when optimizing medical imaging systems for signal detection tasks. However, analytical computation of the IO test statistic…
Progressively-Growing AmbientGANs For Learning Stochastic Object Models From Imaging Measurements
Weimin Zhou, Sayantan Bhadra, Frank J. Brooks +2
The objective optimization of medical imaging systems requires full characterization of all sources of randomness in the measured data, which includes the variability within the en…
Approximating the Ideal Observer and Hotelling Observer for binary signal detection tasks by use of supervised learning methods
Weimin Zhou, Hua Li, Mark A. Anastasio
It is widely accepted that optimization of medical imaging system performance should be guided by task-based measures of image quality (IQ). Task-based measures of IQ quantify the…