Frank J. Brooks

According to our database1, Frank J. Brooks authored at least 17 papers between 2013 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of five.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

On csauthors.net:

Bibliography

2024
Assessing the Capacity of a Denoising Diffusion Probabilistic Model to Reproduce Spatial Context.
IEEE Trans. Medical Imaging, October, 2024

A method for evaluating deep generative models of images for hallucinations in high-order spatial context.
Pattern Recognit. Lett., 2024

Report on the AAPM Grand Challenge on deep generative modeling for learning medical image statistics.
CoRR, 2024

2023
Assessing the Ability of Generative Adversarial Networks to Learn Canonical Medical Image Statistics.
IEEE Trans. Medical Imaging, June, 2023

Evaluating generative stochastic image models using task-based image quality measures.
Proceedings of the Medical Imaging 2023: Image Perception, 2023

2022
Investigating the robustness of a learning-based method for quantitative phase retrieval from propagation-based x-ray phase contrast measurements under laboratory conditions.
CoRR, 2022

Evaluating procedures for establishing generative adversarial network-based stochastic image models in medical imaging.
Proceedings of the Medical Imaging 2022: Image Perception, 2022

Evaluating the capacity of deep generative models to reproduce measurable high-order spatial arrangements in diagnostic images.
Proceedings of the Medical Imaging 2022: Image Processing, 2022

2021
On Hallucinations in Tomographic Image Reconstruction.
IEEE Trans. Medical Imaging, 2021

A Method for Evaluating the Capacity of Generative Adversarial Networks to Reproduce High-order Spatial Context.
CoRR, 2021

Learning stochastic object models from medical imaging measurements by use of advanced AmbientGANs.
CoRR, 2021

Advancing the AmbientGAN for learning stochastic object models.
Proceedings of the Medical Imaging 2021: Image Perception, 2021

Assessing regularization in tomographic imaging via hallucinations in the null space.
Proceedings of the Medical Imaging 2021: Image Perception, 2021

2020
Learning stochastic object models from medical imaging measurements using Progressively-Growing AmbientGANs.
CoRR, 2020

Progressively-Growing AmbientGANs for learning stochastic object models from imaging measurements.
Proceedings of the Medical Imaging 2020: Image Perception, 2020

2019
Learning stochastic object model from noisy imaging measurements using AmbientGANs.
Proceedings of the Medical Imaging 2019: Image Perception, 2019

2013
Quantification of heterogeneity observed in medical images.
BMC Medical Imaging, 2013


  Loading...