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Proceedings Paper

Components of variance in ROC analysis of CADx classifier performance: II. Applications of the bootstrap
Author(s): Robert F. Wagner; Heang-Ping Chan; Berkman Sahiner; Nicholas Petrick; Joseph T. Mossoba
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Paper Abstract

We review components-of-variance models for the uncertainty in estimates of the area under the ROC curve, Az, for the case of classical discriminants where we wish the uncertainty to generalize to a population of training cases as well as to a population of testing cases. A key observation from our previous work facilitates the use of resampling strategies to analyze a finite data set and classifier in terms of the components-of-variance models. In particular, we demonstrate the use of the statistical bootstrap in combination with a four-term variance model to solve for the contributions of the uncertainty in Az that result from a given finite training sample, a given finite test sample, and their interaction. At the same time one obtains an expression from which one can predict the change in uncertainty in estimates of Az that would result from a given change in the number of training samples and change in the number of test samples. This expression provides a quantitative design tool for estimating the size that would be required in a larger pivotal study from the results of a smaller pilot study for the purpose of achieving a desired precision in Az and the desired generalizability.

Paper Details

Date Published: 21 May 1999
PDF: 10 pages
Proc. SPIE 3661, Medical Imaging 1999: Image Processing, (21 May 1999); doi: 10.1117/12.348608
Show Author Affiliations
Robert F. Wagner, Ctr. for Devices and Radiological Health/FDA (United States)
Heang-Ping Chan, Univ. of Michigan (United States)
Berkman Sahiner, Univ. of Michigan (United States)
Nicholas Petrick, Univ. of Michigan (United States)
Joseph T. Mossoba, Ctr. for Devices and Radiological Health/FDA (United States)

Published in SPIE Proceedings Vol. 3661:
Medical Imaging 1999: Image Processing
Kenneth M. Hanson, Editor(s)

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