Learning the Hotelling observer for SKE detection tasks by use of supervised learning methods-Reference-Cited by-同舟云学术

Learning the Hotelling observer for SKE detection tasks by use of supervised learning methods

Published:2019-03-04 Issue: Volume: Page:
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Container-title:Medical Imaging 2019: Image Perception, Observer Performance, and Technology Assessment
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Author:

Zhou Weimin,Li Hua,Anastasio Mark A.

Publisher

SPIE

Reference16 articles.

1. Ideal-observer computation in medical imaging with use of Markov-chain Monte Carlo techniques

2. Ideal-Observer performance under signal and background uncertainty;Park,2003

3. Ideal observer approximation using Bayesian classification neural networks

4. Learning the Ideal Observer for SKE detection tasks by use of convolutional neural networks;Zhou;Medical Imaging 2018: Image Perception, Observer Performance, and Technology Assessment,2018

5. Channelized-ideal observer using Laguerre-Gauss channels in detection tasks involving non-Gaussian distributed lumpy backgrounds and a Gaussian signal

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