Search Results for author: Frank J. Brooks

Found 10 papers, 1 papers with code

Assessing the capacity of a denoising diffusion probabilistic model to reproduce spatial context

no code implementations19 Sep 2023 Rucha Deshpande, Muzaffer Özbey, Hua Li, Mark A. Anastasio, Frank J. Brooks

However, there remains an important need to understand the extent to which DDPMs can reliably learn medical imaging domain-relevant information, which is referred to as `spatial context' in this work.

Data Augmentation Denoising +1

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

no code implementations2 Nov 2022 Rucha Deshpande, Ashish Avachat, Frank J. Brooks, Mark A. Anastasio

In this work, a LBM was assessed for its applicability under practical scenarios by evaluating its robustness and generalizability under typical experimental variations.

Object Retrieval

Assessing the ability of generative adversarial networks to learn canonical medical image statistics

no code implementations26 Apr 2022 Varun A. Kelkar, Dimitrios S. Gotsis, Frank J. Brooks, Prabhat KC, Kyle J. Myers, Rongping Zeng, Mark A. Anastasio

In recent years, generative adversarial networks (GANs) have gained tremendous popularity for potential applications in medical imaging, such as medical image synthesis, restoration, reconstruction, translation, as well as objective image quality assessment.

Image Generation Image Quality Assessment +1

A Method for Evaluating Deep Generative Models of Images via Assessing the Reproduction of High-order Spatial Context

no code implementations24 Nov 2021 Rucha Deshpande, Mark A. Anastasio, Frank J. Brooks

We designed several stochastic context models (SCMs) of distinct image features that can be recovered after generation by a trained GAN.

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

no code implementations27 Jun 2021 Weimin Zhou, Sayantan Bhadra, Frank J. Brooks, Hua Li, Mark A. Anastasio

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.

Generative Adversarial Network

Advancing the AmbientGAN for learning stochastic object models

no code implementations30 Jan 2021 Weimin Zhou, Sayantan Bhadra, Frank J. Brooks, Jason L. Granstedt, Hua Li, Mark A. Anastasio

Medical imaging systems are commonly assessed and optimized by use of objective-measures of image quality (IQ) that quantify the performance of an observer at specific tasks.

Generative Adversarial Network Object

On hallucinations in tomographic image reconstruction

3 code implementations1 Dec 2020 Sayantan Bhadra, Varun A. Kelkar, Frank J. Brooks, Mark A. Anastasio

The behavior of different reconstruction methods under the proposed formalism is discussed with the help of the numerical studies.

Hallucination Image Reconstruction

Learning stochastic object models from medical imaging measurements using Progressively-Growing AmbientGANs

no code implementations29 May 2020 Weimin Zhou, Sayantan Bhadra, Frank J. Brooks, Hua Li, Mark A. Anastasio

To circumvent this, in this work, a new Progressive Growing AmbientGAN (ProAmGAN) strategy is developed for establishing SOMs from medical imaging measurements.

Progressively-Growing AmbientGANs For Learning Stochastic Object Models From Imaging Measurements

no code implementations26 Jan 2020 Weimin Zhou, Sayantan Bhadra, Frank J. Brooks, Hua Li, Mark A. Anastasio

However, because medical imaging systems record imaging measurements that are noisy and indirect representations of object properties, GANs cannot be directly applied to establish stochastic models of objects to-be-imaged.

Object

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