Perceptron error surface analysis: a case study in breast cancer diagnosis.

Published

Journal Article

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).

Full Text

Duke Authors

Cited Authors

  • Markey, MK; Lo, JY; Vargas-Voracek, R; Tourassi, GD; Floyd, CE

Published Date

  • March 2002

Published In

Volume / Issue

  • 32 / 2

Start / End Page

  • 99 - 109

PubMed ID

  • 11879823

Pubmed Central ID

  • 11879823

International Standard Serial Number (ISSN)

  • 0010-4825

Digital Object Identifier (DOI)

  • 10.1016/s0010-4825(01)00035-x

Language

  • eng

Conference Location

  • United States