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Proceedings Paper

Potential reasons for differences in CAD effectiveness evaluated using laboratory and clinical studies
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Paper Abstract

Research studies have investigated a number of factors that may impact the performance assessment of computer aided detection (CAD) effectiveness, such as the inherent design of the CAD, the image and reader samples, and the assessment methods. In this study, we focused on the effect of prevalence on cue validity (co-occurrence of cue and signal) and learning as potentially important factors in CAD assessment. For example, the prevalence of cases with breast cancer is around 50% in laboratory CAD studies, which is 100 times higher than that in breast cancer screening. Although ROC is prevalence-independent, an observer’s use of CAD involves tasks that are more complicated than binary classification, including: search, detection, classification, cueing and learning. We developed models to investigate the potential impact of prevalence on cue validity and the learning of cue validity tasks. We hope this work motivates new studies that investigate previously under-explored factors involved in image interpretation with a new modality in its assessment.

Paper Details

Date Published: 20 March 2015
PDF: 11 pages
Proc. SPIE 9414, Medical Imaging 2015: Computer-Aided Diagnosis, 94141V (20 March 2015); doi: 10.1117/12.2082811
Show Author Affiliations
Xin He, U.S. Food and Drug Administration (United States)
Frank Samuelson, U.S. Food and Drug Administration (United States)
Rongping Zeng, U.S. Food and Drug Administration (United States)
Berkman Sahiner, U.S. Food and Drug Administration (United States)

Published in SPIE Proceedings Vol. 9414:
Medical Imaging 2015: Computer-Aided Diagnosis
Lubomir M. Hadjiiski; Georgia D. Tourassi, Editor(s)

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