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Proceedings Paper

A theoretical investigation of several methods for combining multiple diagnostic assessments
Author(s): Bei Liu; Charles E. Metz; Yulei Jiang
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Paper Abstract

Acquiring multiple images of the same patient (e.g., mediolateral oblique and craniocaudal view mammograms) can, in principle, help improve diagnostic accuracy. We investigated theoretically, in the context of computer-aided diagnosis (CAD), four methods of combining multiple computer outputs obtained from multiple images of the same patient: taking the average, the median, the maximum, or the minimum of the individual assessments. We assumed that multiple computer outputs for each patient are equally accurate and that they can be transformed monotonically to the same pair of truth conditional normal distributions. We found that both the average and the median always produce improved area under the ROC curve (AUC) compared to single-view images, and that the average always performs better than the median. Furthermore, the maximum and the minimum can also produce improved AUCs and can outperform the average under certain situations, but in other situations they can produce worse results than single-view images. Moreover, except for the median, each method can be the best-performing method under specific conditions. Finally, as the strength of correlation between image pairs increases, the maximum and the minimum tend to perform the best more often whereas the average is less often the best performer.

Paper Details

Date Published: 6 April 2005
PDF: 6 pages
Proc. SPIE 5749, Medical Imaging 2005: Image Perception, Observer Performance, and Technology Assessment, (6 April 2005); doi: 10.1117/12.595826
Show Author Affiliations
Bei Liu, The Univ. of Chicago (United States)
Charles E. Metz, The Univ. of Chicago (United States)
Yulei Jiang, The Univ. of Chicago (United States)


Published in SPIE Proceedings Vol. 5749:
Medical Imaging 2005: Image Perception, Observer Performance, and Technology Assessment
Miguel P. Eckstein; Yulei Jiang, Editor(s)

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