Quantification of heterogeneity observed in medical images. - Frank J. Brooks; Perry Grigsby (BMC Medical Imaging, 2013)

Learning stochastic object model from noisy imaging measurements using AmbientGANs. - Weimin Zhou; Sayantan Bhadra; Frank J. Brooks; Mark A. Anastasio (Image Perception, Observer Performance, and Technology Assessment, 2019)

Progressively-Growing AmbientGANs for learning stochastic object models from imaging measurements. - Weimin Zhou; Sayantan Bhadra; Frank J. Brooks; Hua Li 003; Mark A. Anastasio (Image Perception, Observer Performance, and Technology Assessment, 2020)

Learning stochastic object models from medical imaging measurements using Progressively-Growing AmbientGANs. - Weimin Zhou; Sayantan Bhadra; Frank J. Brooks; Hua Li 003; Mark A. Anastasio (CoRR, 2020)

Assessing regularization in tomographic imaging via hallucinations in the null space. - Sayantan Bhadra; Varun A. Kelkar; Frank J. Brooks; Mark A. Anastasio (Image Perception, Observer Performance, and Technology Assessment, 2021)

Advancing the AmbientGAN for learning stochastic object models. - Weimin Zhou; Sayantan Bhadra; Frank J. Brooks; Jason L. Granstedt; Hua Li 003; Mark A. Anastasio (Image Perception, Observer Performance, and Technology Assessment, 2021)

Learning stochastic object models from medical imaging measurements by use of advanced AmbientGANs. - Weimin Zhou; Sayantan Bhadra; Frank J. Brooks; Hua Li 003; Mark A. Anastasio (CoRR, 2021)

A Method for Evaluating the Capacity of Generative Adversarial Networks to Reproduce High-order Spatial Context. - Rucha Deshpande; Mark A. Anastasio; Frank J. Brooks (CoRR, 2021)

On Hallucinations in Tomographic Image Reconstruction. - Sayantan Bhadra; Varun A. Kelkar; Frank J. Brooks; Mark A. Anastasio (IEEE Trans. Medical Imaging, 2021)

Evaluating the capacity of deep generative models to reproduce measurable high-order spatial arrangements in diagnostic images. - Rucha Deshpande; Mark A. Anastasio; Frank J. Brooks (Image Processing, 2022)

Evaluating procedures for establishing generative adversarial network-based stochastic image models in medical imaging. - Varun A. Kelkar; Dimitrios S. Gotsis; Frank J. Brooks; Kyle J. Myers; Prabhat K. C.; Rongping Zeng; Mark A. Anastasio (Image Perception, Observer Performance, and Technology Assessment, 2022)

Investigating the robustness of a learning-based method for quantitative phase retrieval from propagation-based x-ray phase contrast measurements under laboratory conditions. - Rucha Deshpande; Ashish Avachat; Frank J. Brooks; Mark A. Anastasio (CoRR, 2022)

Evaluating generative stochastic image models using task-based image quality measures. - Varun A. Kelkar; Dimitrios S. Gotsis; Rucha Deshpande; Frank J. Brooks; Prabhat K. C.; Kyle J. Myers; Rongping Zeng; Mark A. Anastasio (Image Perception, Observer Performance, and Technology Assessment, 2023)

Assessing the Ability of Generative Adversarial Networks to Learn Canonical Medical Image Statistics. - Varun A. Kelkar; Dimitrios S. Gotsis; Frank J. Brooks; Prabhat K. C.; Kyle J. Myers; Rongping Zeng; Mark A. Anastasio (IEEE Trans. Medical Imaging, 2023)

Report on the AAPM Grand Challenge on deep generative modeling for learning medical image statistics. - Rucha Deshpande; Varun A. Kelkar; Dimitrios S. Gotsis; Prabhat K. C.; Rongping Zeng; Kyle J. Myers; Frank J. Brooks; Mark A. Anastasio (CoRR, 2024)

A method for evaluating deep generative models of images for hallucinations in high-order spatial context. - Rucha Deshpande; Mark A. Anastasio; Frank J. Brooks (Pattern Recognit. Lett., 2024)

Assessing the Capacity of a Denoising Diffusion Probabilistic Model to Reproduce Spatial Context. - Rucha Deshpande; Muzaffer Özbey; Hua Li 003; Mark A. Anastasio; Frank J. Brooks (IEEE Trans. Medical Imaging, 2024)

Evaluation of Machine-generated Biomedical Images via A Tally-based Similarity Measure. - Frank J. Brooks; Rucha Deshpande (CoRR, 2025)