Bayesian Network Decomposition for Modeling Breast Cancer Detection

M. Velikova, N. de Carvalho Ferreira and P. Lucas

in: Proceedings of the 11th Conference on Artificial Intelligence in Medicine, volume 4594 of Lecture Notes in Artificial Intelligence, 2007

DOI Citations found: 7

Abstract

The automated differentiation between benign and malignant abnormalities is a difficult problem in the breast cancer domain. While previous studies consider a single Bayesian network approach, in this paper we propose a novel perspective based on Bayesian network decomposition. We consider three methods that allow for different (levels of) network topological or structural decomposition. Through examples, we demonstrate some advantages of Bayesian network decomposition for the problem at hand: (i) natural and more intuitive representation of breast abnormalities and their features (ii) compact representation and efficient manipulation of large conditional probability tables, and (iii) a possible improvement in the knowledge acquisition and representation processes.

A pdf file of this publication is available for personal use. Enter your e-mail address in the box below and press the button. You will receive an e-mail message with a link to the pdf file.