Learning the ideal observer for SKE detection tasks by use of convolutional neural networks (Cum Laude Poster Award)

Weimin Zhou, Mark A. Anastasio

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

It has been advocated that task-based measures of image quality (IQ) should be employed to evaluate and optimize imaging systems. Task-based measures of IQ quantify the performance of an observer on a medically relevant task. The Bayesian Ideal Observer (IO), which employs complete statistical information of the object and noise, achieves the upper limit of the performance for a binary signal classification task. However, computing the IO performance is generally analytically intractable and can be computationally burdensome when Markov-chain Monte Carlo (MCMC) techniques are employed. In this paper, supervised learning with convolutional neural networks (CNNs) is employed to approximate the IO test statistics for a signal-known-exactly and background-known-exactly (SKE/BKE) binary detection task. The receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) are compared to those produced by the analytically computed IO. The advantages of the proposed supervised learning approach for approximating the IO are demonstrated.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2018
Subtitle of host publicationImage Perception, Observer Performance, and Technology Assessment
EditorsFrank W. Samuelson, Robert M. Nishikawa
PublisherSPIE
ISBN (Electronic)9781510616431
DOIs
StatePublished - Jan 1 2018
Externally publishedYes
EventMedical Imaging 2018: Image Perception, Observer Performance, and Technology Assessment - Houston, United States
Duration: Feb 11 2018Feb 12 2018

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume10577
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2018: Image Perception, Observer Performance, and Technology Assessment
Country/TerritoryUnited States
CityHouston
Period2/11/182/12/18

Keywords

  • Bayesian Ideal Observer
  • convolutional neural networks
  • signal detection theory
  • Supervised learning

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

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