Learning the ideal observer for SKE detection tasks by use of convolutional neural networks. - Weimin Zhou; Mark A. Anastasio (Image Perception, Observer Performance, and Technology Assessment, 2018)

Autoencoder embedding of task-specific information. - Jason L. Granstedt; Weimin Zhou; Mark A. Anastasio (Image Perception, Observer Performance, and Technology Assessment, 2019)

Learning the Hotelling observer for SKE detection tasks by use of supervised learning methods. - Weimin Zhou; Hua Li 003; Mark A. Anastasio (Image Perception, Observer Performance, and Technology Assessment, 2019)

Learning the ideal observer for joint detection and localization tasks by use of convolutional neural networks. - Weimin Zhou; Mark A. Anastasio (Image Perception, Observer Performance, and Technology Assessment, 2019)

Learning stochastic object model from noisy imaging measurements using AmbientGANs. - Weimin Zhou; Sayantan Bhadra; Frank J. Brooks; Mark A. Anastasio (Image Perception, Observer Performance, and Technology Assessment, 2019)

Approximating the Ideal Observer and Hotelling Observer for Binary Signal Detection Tasks by Use of Supervised Learning Methods. - Weimin Zhou; Hua Li 003; Mark A. Anastasio (IEEE Trans. Medical Imaging, 2019)

Learning efficient channels with a dual loss autoencoder. - Jason L. Granstedt; Weimin Zhou; Mark A. Anastasio (Image Perception, Observer Performance, and Technology Assessment, 2020)

Learning numerical observers using unsupervised domain adaptation. - Shenghua He; Weimin Zhou; Hua Li 003; Mark A. Anastasio (Image Perception, Observer Performance, and Technology Assessment, 2020)

Markov-Chain Monte Carlo approximation of the Ideal Observer using generative adversarial networks. - Weimin Zhou; Mark A. Anastasio (Image Perception, Observer Performance, and Technology Assessment, 2020)

Progressively-Growing AmbientGANs for learning stochastic object models from imaging measurements. - Weimin Zhou; Sayantan Bhadra; Frank J. Brooks; Hua Li 003; Mark A. Anastasio (Image Perception, Observer Performance, and Technology Assessment, 2020)

Medical image reconstruction with image-adaptive priors learned by use of generative adversarial networks. - Sayantan Bhadra; Weimin Zhou; Mark A. Anastasio (CoRR, 2020)

Approximating the Hotelling Observer with Autoencoder-Learned Efficient Channels for Binary Signal Detection Tasks. - Jason L. Granstedt; Weimin Zhou; Mark A. Anastasio (CoRR, 2020)

Learning stochastic object models from medical imaging measurements using Progressively-Growing AmbientGANs. - Weimin Zhou; Sayantan Bhadra; Frank J. Brooks; Hua Li 003; Mark A. Anastasio (CoRR, 2020)

Approximating the Ideal Observer for Joint Signal Detection and Localization Tasks by use of Supervised Learning Methods. - Weimin Zhou; Hua Li 003; Mark A. Anastasio (IEEE Trans. Medical Imaging, 2020)

SlabGAN: a method for generating efficient 3D anisotropic medical volumes using generative adversarial networks. - Jason L. Granstedt; Varun A. Kelkar; Weimin Zhou; Mark A. Anastasio (Image Processing, 2021)

Supervised learning-based ideal observer approximation for joint detection and estimation tasks. - Kaiyan Li 002; Weimin Zhou; Hua Li 003; Mark A. Anastasio (Image Perception, Observer Performance, and Technology Assessment, 2021)

Task-based performance evaluation of deep neural network-based image denoising. - Kaiyan Li 002; Weimin Zhou; Hua Li 003; Mark A. Anastasio (Image Perception, Observer Performance, and Technology Assessment, 2021)

A hybrid channelized Hotelling observer for estimating the ideal linear observer for total-variation-based image reconstruction. - John Paul Phillips; Emil Y. Sidky; Greg Ongie; Weimin Zhou; Juan-Pablo Cruz-Bastida; Ingrid S. Reiser; Mark A. Anastasio; Xiaochuan Pan (Image Perception, Observer Performance, and Technology Assessment, 2021)

Advancing the AmbientGAN for learning stochastic object models. - Weimin Zhou; Sayantan Bhadra; Frank J. Brooks; Jason L. Granstedt; Hua Li 003; Mark A. Anastasio (Image Perception, Observer Performance, and Technology Assessment, 2021)

Learning stochastic object models from medical imaging measurements by use of advanced AmbientGANs. - Weimin Zhou; Sayantan Bhadra; Frank J. Brooks; Hua Li 003; Mark A. Anastasio (CoRR, 2021)

Increase in Axial Compressibility in a Spinning Van der Waals Gas. - Yun Liu; Hao Liu 050; Zhen-Guo Fu; Weimin Zhou (Entropy, 2021)

Assessing the Impact of Deep Neural Network-Based Image Denoising on Binary Signal Detection Tasks. - Kaiyan Li 002; Weimin Zhou; Hua Li 003; Mark A. Anastasio (IEEE Trans. Medical Imaging, 2021)

Analyzing neural networks applied to an anatomical simulation of the breast. - Craig K. Abbey; Sourya Sengupta; Weimin Zhou; Andreu Badal; Rongping Zeng; Frank W. Samuelson; Miguel P. Eckstein; Kyle J. Myers; Mark A. Anastasio; Jovan G. Brankov (Image Perception, Observer Performance, and Technology Assessment, 2022)

A deep Q-learning method for optimizing visual search strategies in backgrounds of dynamic noise. - Weimin Zhou; Miguel P. Eckstein (Image Perception, Observer Performance, and Technology Assessment, 2022)

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 002; Weimin Zhou; Hua Li 003; Mark A. Anastasio (IEEE Trans. Medical Imaging, 2022)

Estimating task-based performance bounds for image reconstruction methods by use of learned-ideal observers. - Kaiyan Li 002; Weimin Zhou; Hua Li 003; Mark A. Anastasio (Image Perception, Observer Performance, and Technology Assessment, 2023)

Task-aware denoising autoencoders for establishing efficient channels. - Weimin Zhou (Image Perception, Observer Performance, and Technology Assessment, 2023)

Optimal visual search strategy with inter-saccade response correlations. - Weimin Zhou; Miguel P. Eckstein (Image Perception, Observer Performance, and Technology Assessment, 2023)

Image Dehazing Enhancement Algorithm Based on Mean Guided Filtering. - Weimin Zhou (J. Inf. Process. Syst., 2023)

Ideal Observer Computation by Use of Markov-Chain Monte Carlo With Generative Adversarial Networks. - Weimin Zhou; Umberto Villa; Mark A. Anastasio (IEEE Trans. Medical Imaging, 2023)

Image Super-Resolution Reconstruction of Landslide Based on Real-ESRGAN. - Ruotong Li; Weimin Zhou (CCEAI, 2024)

AmbientCycleGAN for Establishing Interpretable Stochastic Object Models Based on Mathematical Phantoms and Medical Imaging Measurements. - Xichen Xu; Wentao Chen; Weimin Zhou (CoRR, 2024)

Ambient-Pix2PixGAN for Translating Medical Images from Noisy Data. - Wentao Chen; Xichen Xu; Jie Luo; Weimin Zhou (CoRR, 2024)

Unsupervised Generation of Pseudo Normal PET from MRI with Diffusion Model for Epileptic Focus Localization. - Wentao Chen; Jiwei Li; Xichen Xu; Hui Huang; Siyu Yuan; Miao Zhang; Tianming Xu; Jie Luo; Weimin Zhou (CoRR, 2024)

A 32GS/s 8b 16× Time-Interleaved Hybrid ADC with Self-Detection Offset Calibration, DLL-Based TLSB PVT Variation Calibration and VTC Gain Self-Tracking. - Hongzhi Liang; Jun Chang; Yixiao Luo; Zeyu Peng; Weimin Zhou; Li Dang; Yue Cao; Haolin Han; Yi Shen 007; Shubin Liu; Ruixue Ding; Zhangming Zhu (CICC, 2025)

Enhanced FCM for Medical Image Segmentation Using Superpixel and Convolutional Autoencoder. - Weimin Zhou; Yi Gu (ICIC (28), 2025)

A 10-bit 4 GS/s Pipelined-SAR ADC based on loop-unrolled and partial-interleaving. - Weimin Zhou; Zhe Li; Hongzhi Liang; Li Dang; Ruixue Ding; Shubin Liu; Zhangming Zhu (Microelectron. J., 2025)

Task-based Regularization in Penalized Least-Squares for Binary Signal Detection Tasks in Medical Image Denoising. - Wentao Chen; Tianming Xu; Weimin Zhou (CoRR, 2025)

Ambient Denoising Diffusion Generative Adversarial Networks for Establishing Stochastic Object Models from Noisy Image Data. - Xichen Xu; Wentao Chen; Weimin Zhou (CoRR, 2025)

Using gradient of Lagrangian function to compute efficient channels for the ideal observer. - Weimin Zhou (CoRR, 2025)