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Proceedings Paper

Bayesian ANN estimates of three-class ideal observer decision variables for classification of mammographic masses
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Paper Abstract

We are using Bayesian artificial neural networks (BANNs) to classify mammographic masses. We investigated whether a BANN can estimate ideal observer decision variables to distinguish malignant, benign, and false-positive computer detections. Five features were calculated for 143 malignant and 125 benign mass lesions, and for 1049 false-positive computer detections, in 596 mammograms randomly divided into a training and testing set. A BANN was trained on the training set features and applied to the testing set features. We then used a known relation between three-class ideal observer decision variables and that used by a two-class ideal observer when two of three classes are grouped into one class, giving one decision variable for distinguishing malignant from non-malignant detections, and a second for distinguishing true-positive from false-positive computer detections. For comparison, we pooled the training data into two classes in the same two ways and trained two-class BANNs for these two tasks. The three-class BANN decision variables were essentially identical in performance to the specifically trained two-class BANNS. This is consistent with the theoretical observation that three-class ideal observer decision variables are directly related to those used by a two-class ideal observer.

Paper Details

Date Published: 22 May 2003
PDF: 9 pages
Proc. SPIE 5034, Medical Imaging 2003: Image Perception, Observer Performance, and Technology Assessment, (22 May 2003); doi: 10.1117/12.480343
Show Author Affiliations
Darrin C. Edwards, Univ. of Chicago (United States)
Li Lan, Univ. of Chicago (United States)
Charles E. Metz, Univ. of Chicago (United States)
Maryellen Lissak Giger, Univ. of Chicago (United States)
Robert M. Nishikawa, Univ. of Chicago (United States)


Published in SPIE Proceedings Vol. 5034:
Medical Imaging 2003: Image Perception, Observer Performance, and Technology Assessment
Dev P. Chakraborty; Elizabeth A. Krupinski, Editor(s)

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