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

  • Published:2022-02-23 Issue:01 Volume:9 Page:
  • ISSN:2329-4302
  • Container-title:Journal of Medical Imaging
  • language:
  • Short-container-title:J. Med. Imag.

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 10 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. WIN-PDQ: a Wiener-estimator-based projection-domain quantitative SPECT method that accounts for intra-regional uptake heterogeneity;Medical Imaging 2024: Physics of Medical Imaging;2024-04-01

2. Ambient-Pix2PixGAN for translating medical images from noisy data;Medical Imaging 2024: Image Perception, Observer Performance, and Technology Assessment;2024-03-29

3. AmbientCycleGAN for establishing interpretable stochastic object models based on mathematical phantoms and medical imaging measurements;Medical Imaging 2024: Image Perception, Observer Performance, and Technology Assessment;2024-03-29

4. Application of learned ideal observers for estimating task-based performance bounds for computed imaging systems;Journal of Medical Imaging;2024-03-20

5. Synthetic data in radiological imaging: current state and future outlook;BJR|Artificial Intelligence;2024-01
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