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Proceedings Paper

Evaluation Of Medical Technology
Author(s): David F. Preston; Samuel J. Dwyer III; Larry T. Cook; Steven L. Fritz; James E. Goin; Norman L. Martin; Ralph G, Robinson
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Paper Abstract

Shannon's mutual information function is useful in modeling clinical decisions. The equivocation function determines useful bounds on the average probability of error. A model for medical diagnosis using Shannon's uncertainty function is demonstrated. An example is provided to illustrate the importance of the equivocation function.

Paper Details

Date Published: 1 November 1982
PDF: 6 pages
Proc. SPIE 0375, Medical Imaging and Image Interpretation, (1 November 1982); doi: 10.1117/12.934693
Show Author Affiliations
David F. Preston, The University of Kansas (United States)
Samuel J. Dwyer III, The University of Kansas (United States)
Larry T. Cook, The University of Kansas (United States)
Steven L. Fritz, The University of Kansas (United States)
James E. Goin, The University of Kansas (United States)
Norman L. Martin, The University of Kansas (United States)
Ralph G, Robinson, The University of Kansas (United States)


Published in SPIE Proceedings Vol. 0375:
Medical Imaging and Image Interpretation
Judith M. S. Prewitt, Editor(s)

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