Search Results for author:
Found 17 papers, 0 papers with code
Unsupervised Generation of Pseudo Normal PET from MRI with Diffusion Model for Epileptic Focus Localization
In this study, we investigated unsupervised learning methods for unpaired MRI to PET translation for generating pseudo normal FDG PET for epileptic focus localization.
Ambient-Pix2PixGAN for Translating Medical Images from Noisy Data
Recently, an augmented GAN architecture named AmbientGAN has been proposed that can be trained on noisy measurement data to synthesize high-quality clean medical images.
AmbientCycleGAN for Establishing Interpretable Stochastic Object Models Based on Mathematical Phantoms and Medical Imaging Measurements
Ideally, computation of task-based measures of IQ needs to account for all sources of randomness in the measurement data, including the variability in the ensemble of objects to be imaged.
Ideal Observer Computation by Use of Markov-Chain Monte Carlo with Generative Adversarial Networks
Medical imaging systems are often evaluated and optimized via objective, or task-specific, measures of image quality (IQ) that quantify the performance of an observer on a specific clinically-relevant task.
A deep Q-learning method for optimizing visual search strategies in backgrounds of dynamic noise
We demonstrate that the search strategy corresponding to the Q-network is consistent with the IS search strategy.
A Hybrid Approach for Approximating the Ideal Observer for Joint Signal Detection and Estimation Tasks by Use of Supervised Learning and Markov-Chain Monte Carlo Methods
Specifically, a hybrid approach is developed that combines a multi-task convolutional neural network and a Markov-Chain Monte Carlo (MCMC) method in order to approximate the IO for detection-estimation tasks.
Learning stochastic object models from medical imaging measurements by use of advanced ambient generative adversarial networks
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.
Assessing the Impact of Deep Neural Network-based Image Denoising on Binary Signal Detection Tasks
The performance of the ideal observer (IO) and common linear numerical observers are quantified and detection efficiencies are computed to assess the impact of the denoising operation on task performance.
Advancing the AmbientGAN for learning stochastic object models
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.
Learning stochastic object models from medical imaging measurements using Progressively-Growing AmbientGANs
To circumvent this, in this work, a new Progressive Growing AmbientGAN (ProAmGAN) strategy is developed for establishing SOMs from medical imaging measurements.
Approximating the Ideal Observer for joint signal detection and localization tasks by use of supervised learning methods
When joint signal detection and localization tasks are considered, the IO that employs a modified generalized likelihood ratio test maximizes observer performance as characterized by the localization receiver operating characteristic (LROC) curve.
Approximating the Hotelling Observer with Autoencoder-Learned Efficient Channels for Binary Signal Detection Tasks
Overall, AEs are demonstrated to be competitive with state-of-the-art methods for generating efficient channels for the HO and can have superior performance on small datasets.
Learning Numerical Observers using Unsupervised Domain Adaptation
In this study, we propose and investigate the use of an adversarial domain adaptation method to mitigate the deleterious effects of domain shift between simulated and experimental image data for deep learning-based numerical observers (DL-NOs) that are trained on simulated images but applied to experimental ones.
Medical image reconstruction with image-adaptive priors learned by use of generative adversarial networks
Medical image reconstruction is typically an ill-posed inverse problem.
Markov-Chain Monte Carlo Approximation of the Ideal Observer using Generative Adversarial Networks
To approximate the IO test statistic, sampling-based methods that employ Markov-Chain Monte Carlo (MCMC) techniques have been developed.
Progressively-Growing AmbientGANs For Learning Stochastic Object Models From Imaging Measurements
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.
Approximating the Ideal Observer and Hotelling Observer for binary signal detection tasks by use of supervised learning methods
For binary signal detection tasks, the Bayesian Ideal Observer (IO) sets an upper limit of observer performance and has been advocated for use in optimizing medical imaging systems and data-acquisition designs.
