Virtual clinical trials in medical imaging: a review
The accelerating complexity and variety of medical imaging devices and methods have
outpaced the ability to evaluate and optimize their design and clinical use. This is a …
outpaced the ability to evaluate and optimize their design and clinical use. This is a …
Approximating the ideal observer and hotelling observer for binary signal detection tasks by use of supervised learning methods
It is widely accepted that the optimization of medical imaging system performance should be
guided by task-based measures of image quality (IQ). Task-based measures of IQ quantify …
guided by task-based measures of image quality (IQ). Task-based measures of IQ quantify …
A deep learning‐and partial least square regression‐based model observer for a low‐contrast lesion detection task in CT
Purpose This work aims to develop a new framework of image quality assessment using
deep learning‐based model observer (DL‐MO) and to validate it in a low‐contrast lesion …
deep learning‐based model observer (DL‐MO) and to validate it in a low‐contrast lesion …
Learning stochastic object models from medical imaging measurements by use of advanced ambient generative adversarial networks
Purpose: To objectively assess new medical imaging technologies via computer-
simulations, it is important to account for the variability in the ensemble of objects to be …
simulations, it is important to account for the variability in the ensemble of objects to be …
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 objective
measures of image quality (IQ). The Ideal Observer (IO) performance has been advocated to …
measures of image quality (IQ). The Ideal Observer (IO) performance has been advocated to …
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 …
measures of image quality (IQ) that quantify the performance of an observer on a specific …
Markov-chain monte carlo approximation of the ideal observer using generative adversarial networks
W Zhou, MA Anastasio - Medical Imaging 2020: Image …, 2020 - spiedigitallibrary.org
The Ideal Observer (IO) performance has been advocated when optimizing medical imaging
systems for signal detection tasks. However, analytical computation of the IO test statistic is …
systems for signal detection tasks. However, analytical computation of the IO test statistic is …
Task-based performance evaluation of deep neural network-based image denoising
Deep neural network (DNN)-based image denoising methods have been proposed for use
with medical images. These methods are commonly optimized and evaluated by use of …
with medical images. These methods are commonly optimized and evaluated by use of …
Supervised learning-based ideal observer approximation for joint detection and estimation tasks
The ideal observer (IO) sets an upper performance limit among all observers and has been
advocated for use in assessing and optimizing imaging systems. For joint detection …
advocated for use in assessing and optimizing imaging systems. For joint detection …
Learning the Hotelling observer for SKE detection tasks by use of supervised learning methods
Task-based measures of image quality (IQ) quantify the ability of an observer to perform a
specific task. Such measures are commonly employed for assessing and optimizing medical …
specific task. Such measures are commonly employed for assessing and optimizing medical …