194 citations · 1k across the 61 of their papers we have counts for
8 papers · 1 filter
On the impact of incorporating task-information in learning-based image denoising
Kaiyan Li, Hua Li, Mark A. Anastasio
A variety of deep neural network (DNN)-based image denoising methods have been proposed for use with medical images. These methods are typically trained by minimizing loss function…
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 +1
Quantitative phase retrieval (QPR) in propagation-based x-ray phase contrast imaging of heterogeneous and structurally complicated objects is challenging under laboratory condition…
A Memory-Efficient Dynamic Image Reconstruction Method using Neural Fields
Luke Lozenski, Mark A. Anastasio, Umberto Villa
Dynamic imaging is essential for analyzing various biological systems and behaviors but faces two main challenges: data incompleteness and computational burden. For many imaging sy…
Assessing the ability of generative adversarial networks to learn canonical medical image statistics
Varun A. Kelkar, Dimitrios S. Gotsis, Frank J. Brooks +4
In recent years, generative adversarial networks (GANs) have gained tremendous popularity for potential applications in medical imaging, such as medical image synthesis, restoratio…
Evaluating Procedures for Establishing Generative Adversarial Network-based Stochastic Image Models in Medical Imaging
Varun A. Kelkar, Dimitrios S. Gotsis, Frank J. Brooks +4
Modern generative models, such as generative adversarial networks (GANs), hold tremendous promise for several areas of medical imaging, such as unconditional medical image synthesi…
Application of DatasetGAN in medical imaging: preliminary studies
Zong Fan, Varun Kelkar, Mark A. Anastasio +1
Generative adversarial networks (GANs) have been widely investigated for many potential applications in medical imaging. DatasetGAN is a recently proposed framework based on modern…