Weimin Zhou - RankMe

49 papers B 1Journal 27Unranked 21

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	A 0.0013-mm2 7-bit 2-GS/s time-domain 4-bit/cycle SAR ADC with the input-recoverable constant-current VTC. Microelectron. J. Zhe Li, Jun Chang, Weimin Zhou, Li Dang, Hongzhi Liang, Shubin Liu
2025	J	`jnl`	A 10-bit 4 GS/s Pipelined-SAR ADC based on loop-unrolled and partial-interleaving. Microelectron. J. Weimin Zhou, Zhe Li, Hongzhi Liang, Li Dang, Ruixue Ding, Shubin Liu, Zhangming Zhu
2025	—	`conf`	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. CICC Hongzhi Liang, Jun Chang, Yixiao Luo, Zeyu Peng, Weimin Zhou, Li Dang, Yue Cao, Haolin Han, Yi Shen, Shubin Liu, Ruixue Ding, Zhangming Zhu
2025	J	`jnl`	Ambient Denoising Diffusion Generative Adversarial Networks for Establishing Stochastic Object Models from Noisy Image Data. CoRR Xichen Xu, Wentao Chen, Weimin Zhou
2025	—	`conf`	Enhanced FCM for Medical Image Segmentation Using Superpixel and Convolutional Autoencoder. ICIC (28) Weimin Zhou, Yi Gu
2025	J	`jnl`	Task-based Regularization in Penalized Least-Squares for Binary Signal Detection Tasks in Medical Image Denoising. CoRR Wentao Chen, Tianming Xu, Weimin Zhou
2025	J	`jnl`	Using gradient of Lagrangian function to compute efficient channels for the ideal observer. CoRR Weimin Zhou
2024	J	`jnl`	Ambient-Pix2PixGAN for Translating Medical Images from Noisy Data. CoRR Wentao Chen, Xichen Xu, Jie Luo, Weimin Zhou
2024	J	`jnl`	AmbientCycleGAN for Establishing Interpretable Stochastic Object Models Based on Mathematical Phantoms and Medical Imaging Measurements. CoRR Xichen Xu, Wentao Chen, Weimin Zhou
2024	—	`conf`	Image Super-Resolution Reconstruction of Landslide Based on Real-ESRGAN. CCEAI Ruotong Li, Weimin Zhou
2024	J	`jnl`	Unsupervised Generation of Pseudo Normal PET from MRI with Diffusion Model for Epileptic Focus Localization. CoRR Wentao Chen, Jiwei Li, Xichen Xu, Hui Huang, Siyu Yuan, Miao Zhang, Tianming Xu, Jie Luo, Weimin Zhou
2023	—	`conf`	Estimating task-based performance bounds for image reconstruction methods by use of learned-ideal observers. Image Perception, Observer Performance, and Technology Assessment Kaiyan Li, Weimin Zhou, Hua Li, Mark A. Anastasio
2023	J	`jnl`	Ideal Observer Computation by Use of Markov-Chain Monte Carlo With Generative Adversarial Networks. IEEE Trans. Medical Imaging Weimin Zhou, Umberto Villa, Mark A. Anastasio
2023	J	`jnl`	Ideal Observer Computation by Use of Markov-Chain Monte Carlo with Generative Adversarial Networks. CoRR Weimin Zhou, Umberto Villa, Mark A. Anastasio
2023	J	`jnl`	Image Dehazing Enhancement Algorithm Based on Mean Guided Filtering. J. Inf. Process. Syst. Weimin Zhou
2023	—	`conf`	Optimal visual search strategy with inter-saccade response correlations. Image Perception, Observer Performance, and Technology Assessment Weimin Zhou, Miguel P. Eckstein
2023	—	`conf`	Task-aware denoising autoencoders for establishing efficient channels. Image Perception, Observer Performance, and Technology Assessment Weimin Zhou
2022	J	`jnl`	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 Kaiyan Li, Weimin Zhou, Hua Li, Mark A. Anastasio
2022	—	`conf`	A deep Q-learning method for optimizing visual search strategies in backgrounds of dynamic noise. Image Perception, Observer Performance, and Technology Assessment Weimin Zhou, Miguel P. Eckstein
2022	J	`jnl`	A deep Q-learning method for optimizing visual search strategies in backgrounds of dynamic noise. CoRR Weimin Zhou, Miguel P. Eckstein
2022	—	`conf`	Analyzing neural networks applied to an anatomical simulation of the breast. Image Perception, Observer Performance, and Technology Assessment 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
2021	—	`conf`	A hybrid channelized Hotelling observer for estimating the ideal linear observer for total-variation-based image reconstruction. Image Perception, Observer Performance, and Technology Assessment John Paul Phillips, Emil Y. Sidky, Greg Ongie, Weimin Zhou, Juan-Pablo Cruz-Bastida, Ingrid S. Reiser, Mark A. Anastasio, Xiaochuan Pan
2021	—	`conf`	Advancing the AmbientGAN for learning stochastic object models. Image Perception, Observer Performance, and Technology Assessment Weimin Zhou, Sayantan Bhadra, Frank J. Brooks, Jason L. Granstedt, Hua Li, Mark A. Anastasio
2021	J	`jnl`	Advancing the AmbientGAN for learning stochastic object models. CoRR Weimin Zhou, Sayantan Bhadra, Frank J. Brooks, Jason L. Granstedt, Hua Li, Mark A. Anastasio
2021	J	`jnl`	Assessing the Impact of Deep Neural Network-Based Image Denoising on Binary Signal Detection Tasks. IEEE Trans. Medical Imaging Kaiyan Li, Weimin Zhou, Hua Li, Mark A. Anastasio
2021	J	`jnl`	Increase in Axial Compressibility in a Spinning Van der Waals Gas. Entropy Yun Liu, Hao Liu, Zhen-Guo Fu, Weimin Zhou
2021	J	`jnl`	Learning stochastic object models from medical imaging measurements by use of advanced AmbientGANs. CoRR Weimin Zhou, Sayantan Bhadra, Frank J. Brooks, Hua Li, Mark A. Anastasio
2021	B	`conf`	SlabGAN: a method for generating efficient 3D anisotropic medical volumes using generative adversarial networks. Image Processing Jason L. Granstedt, Varun A. Kelkar, Weimin Zhou, Mark A. Anastasio
2021	—	`conf`	Supervised learning-based ideal observer approximation for joint detection and estimation tasks. Image Perception, Observer Performance, and Technology Assessment Kaiyan Li, Weimin Zhou, Hua Li, Mark A. Anastasio
2021	—	`conf`	Task-based performance evaluation of deep neural network-based image denoising. Image Perception, Observer Performance, and Technology Assessment Kaiyan Li, Weimin Zhou, Hua Li, Mark A. Anastasio
2020	J	`jnl`	Approximating the Hotelling Observer with Autoencoder-Learned Efficient Channels for Binary Signal Detection Tasks. CoRR Jason L. Granstedt, Weimin Zhou, Mark A. Anastasio
2020	J	`jnl`	Approximating the Ideal Observer for Joint Signal Detection and Localization Tasks by use of Supervised Learning Methods. IEEE Trans. Medical Imaging Weimin Zhou, Hua Li, Mark A. Anastasio
2020	J	`jnl`	Approximating the Ideal Observer for joint signal detection and localization tasks by use of supervised learning methods. CoRR Weimin Zhou, Hua Li, Mark A. Anastasio
2020	J	`jnl`	Learning Numerical Observers using Unsupervised Domain Adaptation. CoRR Shenghua He, Weimin Zhou, Hua Li, Mark A. Anastasio
2020	—	`conf`	Learning efficient channels with a dual loss autoencoder. Image Perception, Observer Performance, and Technology Assessment Jason L. Granstedt, Weimin Zhou, Mark A. Anastasio
2020	—	`conf`	Learning numerical observers using unsupervised domain adaptation. Image Perception, Observer Performance, and Technology Assessment Shenghua He, Weimin Zhou, Hua Li, Mark A. Anastasio
2020	J	`jnl`	Learning stochastic object models from medical imaging measurements using Progressively-Growing AmbientGANs. CoRR Weimin Zhou, Sayantan Bhadra, Frank J. Brooks, Hua Li, Mark A. Anastasio
2020	J	`jnl`	Markov-Chain Monte Carlo Approximation of the Ideal Observer using Generative Adversarial Networks. CoRR Weimin Zhou, Mark A. Anastasio
2020	—	`conf`	Markov-Chain Monte Carlo approximation of the Ideal Observer using generative adversarial networks. Image Perception, Observer Performance, and Technology Assessment Weimin Zhou, Mark A. Anastasio
2020	J	`jnl`	Medical image reconstruction with image-adaptive priors learned by use of generative adversarial networks. CoRR Sayantan Bhadra, Weimin Zhou, Mark A. Anastasio
2020	J	`jnl`	Progressively-Growing AmbientGANs For Learning Stochastic Object Models From Imaging Measurements. CoRR Weimin Zhou, Sayantan Bhadra, Frank J. Brooks, Hua Li, Mark A. Anastasio
2020	—	`conf`	Progressively-Growing AmbientGANs for learning stochastic object models from imaging measurements. Image Perception, Observer Performance, and Technology Assessment Weimin Zhou, Sayantan Bhadra, Frank J. Brooks, Hua Li, Mark A. Anastasio
2019	J	`jnl`	Approximating the Ideal Observer and Hotelling Observer for Binary Signal Detection Tasks by Use of Supervised Learning Methods. IEEE Trans. Medical Imaging Weimin Zhou, Hua Li, Mark A. Anastasio
2019	J	`jnl`	Approximating the Ideal Observer and Hotelling Observer for binary signal detection tasks by use of supervised learning methods. CoRR Weimin Zhou, Hua Li, Mark A. Anastasio
2019	—	`conf`	Autoencoder embedding of task-specific information. Image Perception, Observer Performance, and Technology Assessment Jason L. Granstedt, Weimin Zhou, Mark A. Anastasio
2019	—	`conf`	Learning stochastic object model from noisy imaging measurements using AmbientGANs. Image Perception, Observer Performance, and Technology Assessment Weimin Zhou, Sayantan Bhadra, Frank J. Brooks, Mark A. Anastasio
2019	—	`conf`	Learning the Hotelling observer for SKE detection tasks by use of supervised learning methods. Image Perception, Observer Performance, and Technology Assessment Weimin Zhou, Hua Li, Mark A. Anastasio
2019	—	`conf`	Learning the ideal observer for joint detection and localization tasks by use of convolutional neural networks. Image Perception, Observer Performance, and Technology Assessment Weimin Zhou, Mark A. Anastasio
2018	—	`conf`	Learning the ideal observer for SKE detection tasks by use of convolutional neural networks. Image Perception, Observer Performance, and Technology Assessment Weimin Zhou, Mark A. Anastasio