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Proceedings Paper

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

We analyze the contributions to the population variance of the area under the ROC curve in assessment of CADx classifier performance and consider a number of models for this variance. The models all contain a pure term or terms in the number of training samples, a pure term in the number of test samples, plus a term or terms representing their interaction. The subset of terms containing the number of test samples also provide a model for what we call the mean Wilcoxon variance based on a single data set. By this variance we mean a nonparametric estimate of the uncertainty in the ROC area obtainable from a single experiment. The remaining terms--i.e., the pure terms in the number of training samples--are not directly estimable without drawing additional training samples. We investigate whether they may be inferred indirectly using a resampling strategy. The current study is presented within the context of our previous work on finite-sample effects on classifier performance, and is related to recent work of others on Analysis of Variance in ROC analysis.

Paper Details

Date Published: 24 June 1998
PDF: 17 pages
Proc. SPIE 3338, Medical Imaging 1998: Image Processing, (24 June 1998); doi: 10.1117/12.310896
Show Author Affiliations
Robert F. Wagner, FDA Ctr. for Devices and Radiological Health (United States)
Heang-Ping Chan, Univ. of Michigan (United States)
Joseph T. Mossoba, FDA Ctr. for Devices and Radiological Health (United States)
Berkman Sahiner, Univ. of Michigan (United States)
Nicholas Petrick, Univ. of Michigan (United States)


Published in SPIE Proceedings Vol. 3338:
Medical Imaging 1998: Image Processing
Kenneth M. Hanson, Editor(s)

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