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Proceedings Paper

Improving the predictive value of mammography using a specialized evolutionary programming hybrid and fitness functions
Author(s): Walker H. Land; Daniel W. McKee; Joseph Y. Lo; Frances R. Anderson
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Paper Abstract

Mammography is an effective tool for the early detection of breast cancer; however, most women referred for biopsy based on mammographic findings do not, have cancer. This study is part of an ongoing effort to reduce the number of benign cases referred for biopsy by developing tools to aid physicians in classifying suspicious lesions. Specifically, this study examines the use of an Evolutionary Programming (EP)/Adaptive Boosting (AB) hybrid, specifically modified to focus on improving the performance of computer-assisted diagnostic (CAD) tools at high specificity levels (missing few or no cancers). An EP/AB hybrid developed by the authors and used in previous studies was modified with two new fitness functions: 1) a function which favored networks with the high PPV values at thresholds corresponding to high sensitivities and 2) a function which favored networks with the highest partial ROC Az (normalized area about 90% sensitivity). The modified hybrid with specialized fitness functions was evaluated using k-fold cross-validation against two real-word mammogram data sets. Results indicate that the number of benign cases referred for biopsy might be reduced by over a third, while missing no cancers. If sensitivity is allowed to decrease to 97% (missing 3% of the cancers), the number of spared biopsies could be raised to over half.

Paper Details

Date Published: 15 May 2003
PDF: 10 pages
Proc. SPIE 5032, Medical Imaging 2003: Image Processing, (15 May 2003); doi: 10.1117/12.483550
Show Author Affiliations
Walker H. Land, Binghamton Univ. (United States)
Daniel W. McKee, Binghamton Univ. (United States)
Joseph Y. Lo, Duke Univ. Medical Ctr. (United States)
Frances R. Anderson, Our Lady of Lourdes Memorial Hospital (United States)


Published in SPIE Proceedings Vol. 5032:
Medical Imaging 2003: Image Processing
Milan Sonka; J. Michael Fitzpatrick, Editor(s)

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