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09/19/2023
Assessing the capacity of a denoising diffusion probabilistic model to reproduce spatial context
Diffusion models have emerged as a popular family of deep generative mod...
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09/09/2023
AmbientFlow: Invertible generative models from incomplete, noisy measurements
Generative models have gained popularity for their potential application...
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06/14/2023
High-Dimensional MR Reconstruction Integrating Subspace and Adaptive Generative Models
We present a novel method that integrates subspace modeling with an adap...
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04/02/2023
Ideal Observer Computation by Use of Markov-Chain Monte Carlo with Generative Adversarial Networks
Medical imaging systems are often evaluated and optimized via objective,...
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03/13/2023
Revisiting model self-interpretability in a decision-theoretic way for binary medical image classification
Interpretability is highly desired for deep neural network-based classif...
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11/02/2022
Investigating the robustness of a learning-based method for quantitative phase retrieval from propagation-based x-ray phase contrast measurements under laboratory conditions
Quantitative phase retrieval (QPR) in propagation-based x-ray phase cont...
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04/26/2022
Assessing the ability of generative adversarial networks to learn canonical medical image statistics
In recent years, generative adversarial networks (GANs) have gained trem...
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04/07/2022
Evaluating Procedures for Establishing Generative Adversarial Network-based Stochastic Image Models in Medical Imaging
Modern generative models, such as generative adversarial networks (GANs)...
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02/27/2022
Application of DatasetGAN in medical imaging: preliminary studies
Generative adversarial networks (GANs) have been widely investigated for...
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02/17/2022
Prior image-based medical image reconstruction using a style-based generative adversarial network
Computed medical imaging systems require a computational reconstruction ...
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02/10/2022
Mining the manifolds of deep generative models for multiple data-consistent solutions of ill-posed tomographic imaging problems
Tomographic imaging is in general an ill-posed inverse problem. Typicall...
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11/24/2021
A Method for Evaluating the Capacity of Generative Adversarial Networks to Reproduce High-order Spatial Context
Generative adversarial networks are a kind of deep generative model with...
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07/06/2021
Impact of deep learning-based image super-resolution on binary signal detection
Deep learning-based image super-resolution (DL-SR) has shown great promi...
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06/27/2021
Learning stochastic object models from medical imaging measurements by use of advanced AmbientGANs
In order to objectively assess new medical imaging technologies via comp...
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01/30/2021
Advancing the AmbientGAN for learning stochastic object models
Medical imaging systems are commonly assessed and optimized by use of ob...
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12/01/2020
On hallucinations in tomographic image reconstruction
Tomographic image reconstruction is generally an ill-posed linear invers...
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11/07/2020
Deeply-Supervised Density Regression for Automatic Cell Counting in Microscopy Images
Accurately counting the number of cells in microscopy images is required...
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05/29/2020
Approximating the Ideal Observer for joint signal detection and localization tasks by use of supervised learning methods
Medical imaging systems are commonly assessed and optimized by use of ob...
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05/29/2020
Learning stochastic object models from medical imaging measurements using Progressively-Growing AmbientGANs
It has been advocated that medical imaging systems and reconstruction al...
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03/04/2020
Approximating the Hotelling Observer with Autoencoder-Learned Efficient Channels for Binary Signal Detection Tasks
The objective assessment of image quality (IQ) has been advocated for th...
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02/03/2020
Learning Numerical Observers using Unsupervised Domain Adaptation
Medical imaging systems are commonly assessed by use of objective image ...
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01/27/2020
Medical image reconstruction with image-adaptive priors learned by use of generative adversarial networks
Medical image reconstruction is typically an ill-posed inverse problem. ...
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01/26/2020
Markov-Chain Monte Carlo Approximation of the Ideal Observer using Generative Adversarial Networks
The Ideal Observer (IO) performance has been advocated when optimizing m...
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01/26/2020
Progressively-Growing AmbientGANs For Learning Stochastic Object Models From Imaging Measurements
The objective optimization of medical imaging systems requires full char...
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05/15/2019
Approximating the Ideal Observer and Hotelling Observer for binary signal detection tasks by use of supervised learning methods
It is widely accepted that optimization of medical imaging system perfor...
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05/14/2019
Reconstruction-Aware Imaging System Ranking by use of a Sparsity-Driven Numerical Observer Enabled by Variational Bayesian Inference
It is widely accepted that optimization of imaging system performance sh...
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05/09/2019
