22 May 2003 Bayesian ANN estimates of three-class ideal observer decision variables for classification of mammographic masses
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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.
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Darrin C. Edwards, Darrin C. Edwards, Li Lan, Li Lan, Charles E. Metz, Charles E. Metz, Maryellen Lissak Giger, Maryellen Lissak Giger, Robert M. Nishikawa, Robert M. Nishikawa, } "Bayesian ANN estimates of three-class ideal observer decision variables for classification of mammographic masses", Proc. SPIE 5034, Medical Imaging 2003: Image Perception, Observer Performance, and Technology Assessment, (22 May 2003); doi: 10.1117/12.480343; https://doi.org/10.1117/12.480343
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