Paper
17 March 2015 Comparison of two stand-alone CADe systems at multiple operating points
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Abstract
Computer-aided detection (CADe) systems are typically designed to work at a given operating point: The device displays a mark if and only if the level of suspiciousness of a region of interest is above a fixed threshold. To compare the standalone performances of two systems, one approach is to select the parameters of the systems to yield a target false-positive rate that defines the operating point, and to compare the sensitivities at that operating point. Increasingly, CADe developers offer multiple operating points, which necessitates the comparison of two CADe systems involving multiple comparisons. To control the Type I error, multiple-comparison correction is needed for keeping the family-wise error rate (FWER) less than a given alpha-level. The sensitivities of a single modality at different operating points are correlated. In addition, the sensitivities of the two modalities at the same or different operating points are also likely to be correlated. It has been shown in the literature that when test statistics are correlated, well-known methods for controlling the FWER are conservative. In this study, we compared the FWER and power of three methods, namely the Bonferroni, step-up, and adjusted step-up methods in comparing the sensitivities of two CADe systems at multiple operating points, where the adjusted step-up method uses the estimated correlations. Our results indicate that the adjusted step-up method has a substantial advantage over other the two methods both in terms of the FWER and power.
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Berkman Sahiner, Weijie Chen, Aria Pezeshk, and Nicholas Petrick "Comparison of two stand-alone CADe systems at multiple operating points", Proc. SPIE 9416, Medical Imaging 2015: Image Perception, Observer Performance, and Technology Assessment, 941612 (17 March 2015); https://doi.org/10.1117/12.2082123
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KEYWORDS
Computer aided diagnosis and therapy

Selenium

Computer aided design

Error analysis

CAD systems

Statistical analysis

Binary data

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