mlsanityData to 14 Oct 2026

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

arXiv 2106.14324 ↗First posted 27 Jun 2021eess.IV cs.CV cs.LGcited by 12

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Abstract (arXiv)

Purpose: To objectively assess new medical imaging technologies via computer-simulations, it is important to account for the variability in the ensemble of objects to be imaged. This source of variability can be described by stochastic object models (SOMs). It is generally desirable to establish SOMs from experimental imaging measurements acquired by use of a well-characterized imaging system, but this task has remained challenging. Approach: A generative adversarial network (GAN)-based method that employs AmbientGANs with modern progressive or multiresolution training approaches is proposed. AmbientGANs established using the proposed training procedure are systematically validated in a controlled way using computer-simulated magnetic resonance imaging (MRI) data corresponding to a stylized imaging system. Emulated single-coil experimental MRI data are also employed to demonstrate the methods under less stylized conditions. Results: The proposed AmbientGAN method can generate clean images when the imaging measurements are contaminated by measurement noise. When the imaging measurement data are incomplete, the proposed AmbientGAN can reliably learn the distribution of the measurement components of the objects. Conclusions: Both visual examinations and quantitative analyses, including task-specific validations using the Hotelling observer, demonstrated that the proposed AmbientGAN method holds promise to establish realistic SOMs from imaging measurements.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 26 of its 74 references that are in the map. First posted on arXiv 27 Jun 2021.

Builds on

26 of 74 references.

References · 26

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14 more
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Cited by year

Citing papers per year: 2022: 1; 2023: 4; 2024: 4; 2025: 3.

12 papers.

  1. 2022Assessing the ability of generative adversarial networks to learn canonical medical image statisticscited by 11
  2. 2023AmbientFlow: Invertible generative models from incomplete, noisy measurementscited by 11
  3. 2023Knowledge-based in silico models and dataset for the comparative evaluation of mammography AI for a range of breast characteristics, lesion conspicuities and dosescited by 8
  4. 2025Regression is all you need for medical image translationcited by 8
  5. 2024Synthetic Data in Radiological Imaging: Current State and Future Outlookcited by 7
  6. 2023The stochastic digital human is now enrolling for in silico imaging trials -- Methods and tools for generating digital cohortscited by 6
  7. 2023Ideal Observer Computation by Use of Markov-Chain Monte Carlo with Generative Adversarial Networkscited by 6
  8. 2024Report on the AAPM Grand Challenge on deep generative modeling for learning medical image statisticscited by 4
  9. 2024Ambient-Pix2PixGAN for Translating Medical Images from Noisy Datacited by 3
  10. 2024AmbientCycleGAN for Establishing Interpretable Stochastic Object Models Based on Mathematical Phantoms and Medical Imaging Measurementscited by 2
  11. 2025Ambient Denoising Diffusion Generative Adversarial Networks for Establishing Stochastic Object Models from Noisy Image Datacited by 2
  12. 2025Establishing Stochastic Object Models from Noisy Data via Ambient Measurement-Integrated Diffusioncited by 0