title: Perceptron error surface analysis: a case study in breast cancer diagnosis.
abstract: Perceptrons are typically trained to minimize mean square error (MSE). In computer-aided diagnosis (CAD), model performance is usually evaluated according to other more clinically relevant measures. The purpose of this study was to investigate the relationship between MSE and the area (A(z)) under the receiver operating characteristic (ROC) curve and the high-sensitivity partial ROC area ((0.90)A'(z)). A perceptron was used to predict lesion malignancy based on two mammographic findings and patient age. For each performance measure, the error surface in weight space was visualized. Comparison of the surfaces indicated that minimizing MSE tended to maximize A(z), but not (0.90)A'(z).
authors.affiliation: Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA. markey@duke.edu
authors.forename: Mia K
authors.initials: MK
authors.lastname: Markey
authors.forename: Joseph Y
authors.initials: JY
authors.lastname: Lo
authors.forename: Rene
authors.initials: R
authors.lastname: Vargas-Voracek
authors.forename: Georgia D
authors.initials: GD
authors.lastname: Tourassi
authors.forename: Carey E
authors.initials: CE
authors.lastname: Floyd
mesh_terms: D001940:Breast / Q000473:pathology; D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D003936:Diagnosis, Computer-Assisted; D003937:Diagnosis, Differential; D003951:Diagnostic Errors; D005260:Female; D006801:Humans; D008327:Mammography; D016571:Neural Networks, Computer