🏆 Finalist — NIH Data Sharing Index (“S-Index”) Challenge

Weimin Zhou

ORCID: 0000-0001-6678-4088
MedicineComputer Science

Pilot corpus only

This score is computed over theSindex pilot corpus and does not cover the full scientific literature. Scores are relative to papers we have ingested — papers, authors, and institutions outside the pilot are not represented. Methodology.

Top 100%percentile
0Author DataRank

Indexed papers

0in pilot corpus
datarank_citation_only_1hop_v6· scope data_onlyMethodology

Papers

Driven by 11 papers. Top paper: “Approximating the Ideal Observer for Joint Signal Detection and Localization Tasks by use of Supervised Learning Methods”.

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

IEEE Transactions on Medical Imaging(2020)10.1109/tmi.2020.3009022
N/A
0.953DataRank · unranked
14 citations

Hua Li, Mark A. Anastasio, Weimin Zhou

Progressively-Growing AmbientGANs For Learning Stochastic Object Models From Imaging Measurements

arXiv (Cornell University)(2020)10.48550/arxiv.2001.09523
N/A
0DataRank · unranked
1 citations

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

Learning Numerical Observers using Unsupervised Domain Adaptation

arXiv (Cornell University)(2020)10.48550/arxiv.2002.03763
N/A
0DataRank · unranked
0 citations

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

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

IEEE Transactions on Medical Imaging(2021)10.1109/tmi.2021.3076810
N/A
0DataRank · unranked
67 citations

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

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 Transactions on Medical Imaging(2021)10.1109/tmi.2021.3135147
N/A
0DataRank · unranked
13 citations

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

A signal detection model for quantifying overregularization in nonlinear image reconstruction

Medical Physics(2021)10.1002/mp.14703
N/A
0DataRank · unranked
4 citations

John Paul Phillips, Weimin Zhou, Greg Ongie, Juan P. Cruz‐Bastida, Ingrid Reiser +3 more

Advancing the AmbientGAN for learning stochastic object models

arXiv (Cornell University)(2021)10.48550/arxiv.2102.00281
N/A
0DataRank · unranked
1 citations

Sayantan Bhadra, Frank J. Brooks, Jason Granstedt, Hua Li, Mark A. Anastasio +1 more

Ideal Observer Computation by Use of Markov-Chain Monte Carlo With Generative Adversarial Networks

IEEE Transactions on Medical Imaging(2023)10.1109/tmi.2023.3304907
N/A
0DataRank · unranked
11 citations

Umberto Villa, Mark A. Anastasio, Weimin Zhou

Approximating the Hotelling observer with autoencoder-learned efficient channels for binary signal detection tasks

Journal of Medical Imaging(2023)10.1117/1.jmi.10.5.055501
N/A
0DataRank · unranked
5 citations

Weimin Zhou, Mark A. Anastasio, Jason Granstedt

Ideal Observer Computation by Use of Markov-Chain Monte Carlo with Generative Adversarial Networks

arXiv (Cornell University)(2023)10.48550/arxiv.2304.00433
N/A
0DataRank · unranked
0 citations

Umberto Villa, Mark A. Anastasio, Weimin Zhou

Approximating the ideal observer for joint signal detection and estimation tasks by the use of Markov-Chain Monte Carlo with generative adversarial networks

Journal of Medical Imaging(2025)10.1117/1.jmi.12.5.051810
N/A
0DataRank · unranked
0 citations

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