Learning stochastic object models from medical imaging measurements by use of advanced ambient generative adversarial networks

Author:

Zhou Weimin1,Bhadra Sayantan2,Brooks Frank J.3,Li Hua3,Anastasio Mark A.3

Affiliation:

1. University of California Santa Barbara, Department of Psychological and Brain Sciences, Santa Barbar

2. Washington University in St. Louis, Department of Computer Science and Engineering, St. Louis, Misso

3. University of Illinois at Urbana-Champaign, Department of Bioengineering, Urbana, Illinois

Publisher

SPIE-Intl Soc Optical Eng

Subject

Radiology, Nuclear Medicine and imaging

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Ideal Observer Computation by Use of Markov-Chain Monte Carlo With Generative Adversarial Networks;IEEE Transactions on Medical Imaging;2023-12

2. The stochastic digital human is now enrolling for in silico imaging trials—methods and tools for generating digital cohorts;Progress in Biomedical Engineering;2023-10-01

3. Optimal visual search strategy with inter-saccade response correlations;Medical Imaging 2023: Image Perception, Observer Performance, and Technology Assessment;2023-04-03

4. Estimating task-based performance bounds for image reconstruction methods by use of learned-ideal observers;Medical Imaging 2023: Image Perception, Observer Performance, and Technology Assessment;2023-04-03

5. Evaluating generative stochastic image models using task-based image quality measures;Medical Imaging 2023: Image Perception, Observer Performance, and Technology Assessment;2023-04-03

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