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2020 – today
- 2024
[c20]Ruotong Li
, Weimin Zhou:
Image Super-Resolution Reconstruction of Landslide Based on Real-ESRGAN. CCEAI 2024: 203-208- [i15]Xichen Xu, Wentao Chen, Weimin Zhou:
AmbientCycleGAN for Establishing Interpretable Stochastic Object Models Based on Mathematical Phantoms and Medical Imaging Measurements. CoRR abs/2402.01171 (2024) - [i14]Wentao Chen, Xichen Xu, Jie Luo, Weimin Zhou:
Ambient-Pix2PixGAN for Translating Medical Images from Noisy Data. CoRR abs/2402.01186 (2024) - [i13]Wentao Chen, Jiwei Li, Xichen Xu, Hui Huang, Siyu Yuan, Miao Zhang, Tianming Xu, Jie Luo, Weimin Zhou:
Unsupervised Generation of Pseudo Normal PET from MRI with Diffusion Model for Epileptic Focus Localization. CoRR abs/2402.01191 (2024) - 2023
- [j7]Weimin Zhou:
Image Dehazing Enhancement Algorithm Based on Mean Guided Filtering. J. Inf. Process. Syst. 19(4): 417-426 (2023) - [j6]Weimin Zhou, Umberto Villa, Mark A. Anastasio:
Ideal Observer Computation by Use of Markov-Chain Monte Carlo With Generative Adversarial Networks. IEEE Trans. Medical Imaging 42(12): 3715-3724 (2023) - [c19]Kaiyan Li, Weimin Zhou, Hua Li, Mark A. Anastasio:
Estimating task-based performance bounds for image reconstruction methods by use of learned-ideal observers. Image Perception, Observer Performance, and Technology Assessment 2023 - [c18]Weimin Zhou:
Task-aware denoising autoencoders for establishing efficient channels. Image Perception, Observer Performance, and Technology Assessment 2023 - [c17]Weimin Zhou, Miguel P. Eckstein:
Optimal visual search strategy with inter-saccade response correlations. Image Perception, Observer Performance, and Technology Assessment 2023 - [i12]Weimin Zhou, Umberto Villa, Mark A. Anastasio:
Ideal Observer Computation by Use of Markov-Chain Monte Carlo with Generative Adversarial Networks. CoRR abs/2304.00433 (2023) - 2022
- [j5]Kaiyan Li, Weimin Zhou, Hua Li, Mark A. Anastasio:
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. IEEE Trans. Medical Imaging 41(5): 1114-1124 (2022) - [c16]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:
Analyzing neural networks applied to an anatomical simulation of the breast. Image Perception, Observer Performance, and Technology Assessment 2022 - [c15]Weimin Zhou, Miguel P. Eckstein:
A deep Q-learning method for optimizing visual search strategies in backgrounds of dynamic noise. Image Perception, Observer Performance, and Technology Assessment 2022 - [i11]Weimin Zhou, Miguel P. Eckstein:
A deep Q-learning method for optimizing visual search strategies in backgrounds of dynamic noise. CoRR abs/2201.12385 (2022) - 2021
- [j4]Yun Liu, Hao Liu, Zhen-Guo Fu, Weimin Zhou:
Increase in Axial Compressibility in a Spinning Van der Waals Gas. Entropy 23(2): 137 (2021) - [j3]Kaiyan Li, Weimin Zhou, Hua Li, Mark A. Anastasio:
Assessing the Impact of Deep Neural Network-Based Image Denoising on Binary Signal Detection Tasks. IEEE Trans. Medical Imaging 40(9): 2295-2305 (2021) - [c14]Jason L. Granstedt, Varun A. Kelkar, Weimin Zhou, Mark A. Anastasio:
SlabGAN: a method for generating efficient 3D anisotropic medical volumes using generative adversarial networks. Image Processing 2021 - [c13]Kaiyan Li, Weimin Zhou, Hua Li, Mark A. Anastasio:
Supervised learning-based ideal observer approximation for joint detection and estimation tasks. Image Perception, Observer Performance, and Technology Assessment 2021 - [c12]Kaiyan Li, Weimin Zhou, Hua Li, Mark A. Anastasio:
Task-based performance evaluation of deep neural network-based image denoising. Image Perception, Observer Performance, and Technology Assessment 2021 - [c11]John Paul Phillips, Emil Y. Sidky, Greg Ongie, Weimin Zhou, Juan-Pablo Cruz-Bastida, Ingrid S. Reiser, Mark A. Anastasio, Xiaochuan Pan:
A hybrid channelized Hotelling observer for estimating the ideal linear observer for total-variation-based image reconstruction. Image Perception, Observer Performance, and Technology Assessment 2021 - [c10]Weimin Zhou, Sayantan Bhadra, Frank J. Brooks, Jason L. Granstedt, Hua Li, Mark A. Anastasio:
Advancing the AmbientGAN for learning stochastic object models. Image Perception, Observer Performance, and Technology Assessment 2021 - [i10]Weimin Zhou, Sayantan Bhadra, Frank J. Brooks, Jason L. Granstedt, Hua Li, Mark A. Anastasio:
Advancing the AmbientGAN for learning stochastic object models. CoRR abs/2102.00281 (2021) - [i9]Weimin Zhou, Sayantan Bhadra, Frank J. Brooks, Hua Li, Mark A. Anastasio:
Learning stochastic object models from medical imaging measurements by use of advanced AmbientGANs. CoRR abs/2106.14324 (2021) - 2020
- [j2]Weimin Zhou, Hua Li, Mark A. Anastasio:
Approximating the Ideal Observer for Joint Signal Detection and Localization Tasks by use of Supervised Learning Methods. IEEE Trans. Medical Imaging 39(12): 3992-4000 (2020) - [c9]Jason L. Granstedt, Weimin Zhou, Mark A. Anastasio:
Learning efficient channels with a dual loss autoencoder. Image Perception, Observer Performance, and Technology Assessment 2020: 113160C - [c8]Shenghua He, Weimin Zhou, Hua Li, Mark A. Anastasio:
Learning numerical observers using unsupervised domain adaptation. Image Perception, Observer Performance, and Technology Assessment 2020: 113160W - [c7]Weimin Zhou, Mark A. Anastasio:
Markov-Chain Monte Carlo approximation of the Ideal Observer using generative adversarial networks. Image Perception, Observer Performance, and Technology Assessment 2020: 113160D - [c6]Weimin Zhou, Sayantan Bhadra, Frank J. Brooks, Hua Li, Mark A. Anastasio:
Progressively-Growing AmbientGANs for learning stochastic object models from imaging measurements. Image Perception, Observer Performance, and Technology Assessment 2020: 113160Q - [i8]Weimin Zhou, Sayantan Bhadra, Frank J. Brooks, Hua Li, Mark A. Anastasio:
Progressively-Growing AmbientGANs For Learning Stochastic Object Models From Imaging Measurements. CoRR abs/2001.09523 (2020) - [i7]Weimin Zhou, Mark A. Anastasio:
Markov-Chain Monte Carlo Approximation of the Ideal Observer using Generative Adversarial Networks. CoRR abs/2001.09526 (2020) - [i6]Sayantan Bhadra, Weimin Zhou, Mark A. Anastasio:
Medical image reconstruction with image-adaptive priors learned by use of generative adversarial networks. CoRR abs/2001.10830 (2020) - [i5]Shenghua He, Weimin Zhou, Hua Li, Mark A. Anastasio:
Learning Numerical Observers using Unsupervised Domain Adaptation. CoRR abs/2002.03763 (2020) - [i4]Jason L. Granstedt, Weimin Zhou, Mark A. Anastasio:
Approximating the Hotelling Observer with Autoencoder-Learned Efficient Channels for Binary Signal Detection Tasks. CoRR abs/2003.02321 (2020) - [i3]Weimin Zhou, Sayantan Bhadra, Frank J. Brooks, Hua Li, Mark A. Anastasio:
Learning stochastic object models from medical imaging measurements using Progressively-Growing AmbientGANs. CoRR abs/2006.00033 (2020) - [i2]Weimin Zhou, Hua Li, Mark A. Anastasio:
Approximating the Ideal Observer for joint signal detection and localization tasks by use of supervised learning methods. CoRR abs/2006.00112 (2020)
2010 – 2019
- 2019
- [j1]Weimin Zhou, Hua Li, Mark A. Anastasio:
Approximating the Ideal Observer and Hotelling Observer for Binary Signal Detection Tasks by Use of Supervised Learning Methods. IEEE Trans. Medical Imaging 38(10): 2456-2468 (2019) - [c5]Weimin Zhou, Sayantan Bhadra, Frank J. Brooks, Mark A. Anastasio:
Learning stochastic object model from noisy imaging measurements using AmbientGANs. Image Perception, Observer Performance, and Technology Assessment 2019: 109520M - [c4]Jason L. Granstedt, Weimin Zhou, Mark A. Anastasio:
Autoencoder embedding of task-specific information. Image Perception, Observer Performance, and Technology Assessment 2019: 1095207 - [c3]Weimin Zhou, Hua Li, Mark A. Anastasio:
Learning the Hotelling observer for SKE detection tasks by use of supervised learning methods. Image Perception, Observer Performance, and Technology Assessment 2019: 1095208 - [c2]Weimin Zhou, Mark A. Anastasio:
Learning the ideal observer for joint detection and localization tasks by use of convolutional neural networks. Image Perception, Observer Performance, and Technology Assessment 2019: 1095209 - [i1]Weimin Zhou, Hua Li, Mark A. Anastasio:
Approximating the Ideal Observer and Hotelling Observer for binary signal detection tasks by use of supervised learning methods. CoRR abs/1905.06330 (2019) - 2018
- [c1]Weimin Zhou, Mark A. Anastasio:
Learning the ideal observer for SKE detection tasks by use of convolutional neural networks. Image Perception, Observer Performance, and Technology Assessment 2018: 1057719
Coauthor Index
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