Weimin Zhou

According to our database, Weimin Zhou authored at least 34 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

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

Ambient-Pix2PixGAN for Translating Medical Images from Noisy Data.

Wentao Chen, Xichen Xu, Jie Luo, Weimin Zhou

CoRR, 2024

AmbientCycleGAN for Establishing Interpretable Stochastic Object Models Based on Mathematical Phantoms and Medical Imaging Measurements.

Xichen Xu, Wentao Chen, Weimin Zhou

CoRR, 2024

Image Super-Resolution Reconstruction of Landslide Based on Real-ESRGAN.

Ruotong Li, Weimin Zhou

Proceedings of the 2024 8th International Conference on Control Engineering and Artificial Intelligence, 2024

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, December, 2023

Image Dehazing Enhancement Algorithm Based on Mean Guided Filtering.

Weimin Zhou

J. Inf. Process. Syst., August, 2023

Optimal visual search strategy with inter-saccade response correlations.

Weimin Zhou, Miguel P. Eckstein

Proceedings of the Medical Imaging 2023: Image Perception, 2023

Task-aware denoising autoencoders for establishing efficient channels.

Weimin Zhou

Proceedings of the Medical Imaging 2023: Image Perception, 2023

Estimating task-based performance bounds for image reconstruction methods by use of learned-ideal observers.

Kaiyan Li, Weimin Zhou, Hua Li, Mark A. Anastasio

Proceedings of the Medical Imaging 2023: Image Perception, 2023

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, Weimin Zhou, Hua Li, Mark A. Anastasio

IEEE Trans. Medical Imaging, 2022

A deep Q-learning method for optimizing visual search strategies in backgrounds of dynamic noise.

Weimin Zhou, Miguel P. Eckstein

Proceedings of the Medical Imaging 2022: Image Perception, 2022

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

Proceedings of the Medical Imaging 2022: Image Perception, 2022

2021

Assessing the Impact of Deep Neural Network-Based Image Denoising on Binary Signal Detection Tasks.

Kaiyan Li, Weimin Zhou, Hua Li, Mark A. Anastasio

IEEE Trans. Medical Imaging, 2021

Increase in Axial Compressibility in a Spinning Van der Waals Gas.

Yun Liu, Hao Liu, Zhen-Guo Fu, Weimin Zhou

Entropy, 2021

Learning stochastic object models from medical imaging measurements by use of advanced AmbientGANs.

Weimin Zhou, Sayantan Bhadra, Frank J. Brooks, Hua Li, Mark A. Anastasio

CoRR, 2021

Advancing the AmbientGAN for learning stochastic object models.

Weimin Zhou, Sayantan Bhadra, Frank J. Brooks, Jason L. Granstedt, Hua Li, Mark A. Anastasio

Proceedings of the Medical Imaging 2021: Image Perception, 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

Proceedings of the Medical Imaging 2021: Image Perception, 2021

Task-based performance evaluation of deep neural network-based image denoising.

Kaiyan Li, Weimin Zhou, Hua Li, Mark A. Anastasio

Proceedings of the Medical Imaging 2021: Image Perception, 2021

Supervised learning-based ideal observer approximation for joint detection and estimation tasks.

Kaiyan Li, Weimin Zhou, Hua Li, Mark A. Anastasio

Proceedings of the Medical Imaging 2021: Image Perception, 2021

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

Proceedings of the Medical Imaging 2021: Image Processing, Online, February 15-19, 2021, 2021

2020

Approximating the Ideal Observer for Joint Signal Detection and Localization Tasks by use of Supervised Learning Methods.

Weimin Zhou, Hua Li, Mark A. Anastasio

IEEE Trans. Medical Imaging, 2020

Learning stochastic object models from medical imaging measurements using Progressively-Growing AmbientGANs.

Weimin Zhou, Sayantan Bhadra, Frank J. Brooks, Hua Li, 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

Medical image reconstruction with image-adaptive priors learned by use of generative adversarial networks.

Sayantan Bhadra, Weimin Zhou, Mark A. Anastasio

CoRR, 2020

Progressively-Growing AmbientGANs for learning stochastic object models from imaging measurements.

Weimin Zhou, Sayantan Bhadra, Frank J. Brooks, Hua Li, Mark A. Anastasio

Proceedings of the Medical Imaging 2020: Image Perception, 2020

Markov-Chain Monte Carlo approximation of the Ideal Observer using generative adversarial networks.

Weimin Zhou, Mark A. Anastasio

Proceedings of the Medical Imaging 2020: Image Perception, 2020

Learning numerical observers using unsupervised domain adaptation.

Shenghua He, Weimin Zhou, Hua Li, Mark A. Anastasio

Proceedings of the Medical Imaging 2020: Image Perception, 2020

Learning efficient channels with a dual loss autoencoder.

Jason L. Granstedt, Weimin Zhou, Mark A. Anastasio

Proceedings of the Medical Imaging 2020: Image Perception, 2020

2019

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

IEEE Trans. Medical Imaging, 2019

Learning stochastic object model from noisy imaging measurements using AmbientGANs.

Weimin Zhou, Sayantan Bhadra, Frank J. Brooks, Mark A. Anastasio

Proceedings of the Medical Imaging 2019: Image Perception, 2019

Learning the ideal observer for joint detection and localization tasks by use of convolutional neural networks.

Weimin Zhou, Mark A. Anastasio

Proceedings of the Medical Imaging 2019: Image Perception, 2019

Learning the Hotelling observer for SKE detection tasks by use of supervised learning methods.

Weimin Zhou, Hua Li, Mark A. Anastasio

Proceedings of the Medical Imaging 2019: Image Perception, 2019

Autoencoder embedding of task-specific information.

Jason L. Granstedt, Weimin Zhou, Mark A. Anastasio

Proceedings of the Medical Imaging 2019: Image Perception, 2019

2018

Learning the ideal observer for SKE detection tasks by use of convolutional neural networks.

Weimin Zhou, Mark A. Anastasio

Proceedings of the Medical Imaging 2018: Image Perception, 2018