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 …
Convolutional Neural Network Model Observers Discount Signal-like Anatomical Structures During Search in Virtual Digital Breast Tomosynthesis Phantoms
Model observers are computational tools to evaluate and optimize task-based medical
image quality. Linear model observers, such as the Channelized Hotelling Observer (CHO) …
image quality. Linear model observers, such as the Channelized Hotelling Observer (CHO) …
Learning numerical observers using unsupervised domain adaptation
Medical imaging systems are commonly assessed by use of objective image quality
measures. Supervised deep learning methods have been investigated to implement …
measures. Supervised deep learning methods have been investigated to implement …
A deep Q-learning method for optimizing visual search strategies in backgrounds of dynamic noise
W Zhou, MP Eckstein - Medical Imaging 2022: Image …, 2022 - spiedigitallibrary.org
Humans process visual information with varying resolution (foveated visual system) and
explore images by orienting through eye movements the high-resolution fovea to points of …
explore images by orienting through eye movements the high-resolution fovea to points of …
Progressively-growing ambientgans for learning stochastic object models from imaging measurements
The objective optimization of medical imaging systems requires full characterization of all
sources of randomness in the measured data, which includes the variability within the …
sources of randomness in the measured data, which includes the variability within the …
[HTML][HTML] Evaluation of convolutional neural networks for search in 1/f 2.8 filtered noise and digital breast tomosynthesis phantoms
With the advent of powerful convolutional neural networks (CNNs), recent studies have
extended early applications of neural networks to imaging tasks thus making CNNs a …
extended early applications of neural networks to imaging tasks thus making CNNs a …