Formal contaminated binormal model and fits of the model to classic ROC data

Donald D. Dorfman; Kevin S. Berbaum

doi:10.1117/12.431181

26 June 2001 Formal contaminated binormal model and fits of the model to classic ROC data

Donald D. Dorfman, Kevin S. Berbaum

Proceedings Volume 4324, Medical Imaging 2001: Image Perception and Performance; (2001) https://doi.org/10.1117/12.431181
Event: Medical Imaging 2001, 2001, San Diego, CA, United States

Abstract

A contaminated binormal receiver operating characteristic (ROC) model is proposed to account for ROC data with very few false positive reports even though many normal patients are sampled. The model assumes that for a proportion of abnormalities, no signal information is captured and that those abnormalities have the same distribution as noise along the latent decision axis. The new model can fit ROC data in which some or all of the ROC points have false positive fractions of zero and true positive fractions less than one without concluding perfect performance. The resulting ROC curves never exhibit inappropriate chance line crossings. The model holds that, for expert decision makers, there are situations in which the prevalence and utility matrix preclude operating points in some ROC regions. The model has a straightforward extension to the joint detection and localization ROC curve. Fits of the contaminated binormal ROC model to non-degenerate data from exemplary experiments in radiology were evaluated. For several studies, the contaminated binormal model fit the data better than conventional ROC models suggesting that contamination may not be limited to degenerate ROC data. This research has been published for a different audience.

Citation Download Citation

Donald D. Dorfman and Kevin S. Berbaum "Formal contaminated binormal model and fits of the model to classic ROC data", Proc. SPIE 4324, Medical Imaging 2001: Image Perception and Performance, (26 June 2001); https://doi.org/10.1117/12.431181

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KEYWORDS

Data modeling

Statistical analysis

Interference (communication)

Statistical modeling

Visualization

Performance modeling

Affine motion model

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