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Proceedings Paper

Markov-Chain Monte Carlo approximation of the Ideal Observer using generative adversarial networks
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Paper Abstract

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 generally intractable. To approximate the IO test statistic, sampling-based methods that employ Markov-Chain Monte Carlo (MCMC) techniques have been developed. However, current applications of MCMC techniques have been limited to several object models such as a lumpy object model and a binary texture model, and it remains unclear how MCMC methods can be implemented with other more sophisticated object models. Deep learning methods that employ generative adversarial networks (GANs) hold great promise to learn stochastic object models (SOMs) from image data. In this study, we described a method to approximate the IO by applying MCMC techniques to SOMs learned by use of GANs. The proposed method can be employed with arbitrary object models that can be learned by use of GANs, thereby the domain of applicability of MCMC techniques for approximating the IO performance is extended. In this study, both signal-known-exactly (SKE) and signal-known-statistically (SKS) binary signal detection tasks are considered. The IO performance computed by the proposed method is compared to that computed by the conventional MCMC method. The advantages of the proposed method are discussed.

Paper Details

Date Published: 16 March 2020
PDF: 7 pages
Proc. SPIE 11316, Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment, 113160D (16 March 2020); doi: 10.1117/12.2549732
Show Author Affiliations
Weimin Zhou, Washington Univ. in St. Louis (United States)
Mark A. Anastasio, Univ. of Illinois at Urbana-Champaign (United States)

Published in SPIE Proceedings Vol. 11316:
Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment
Frank W. Samuelson; Sian Taylor-Phillips, Editor(s)

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