Bayesian Network Decomposition for Modeling Breast Cancer Detection

Authors:
Marina Velikova;Nivea Carvalho Ferreira;Peter Lucas
Affiliations:
Department of Radiology, Radboud University Nijmegen Medical Centre, 6525 GA, Nijmegen, The Netherlands;Institute for Computing and Information Sciences, Radboud University Nijmegen, 6525 ED Nijmegen, The Netherlands;Institute for Computing and Information Sciences, Radboud University Nijmegen, 6525 ED Nijmegen, The Netherlands
Venue:
AIME '07 Proceedings of the 11th conference on Artificial Intelligence in Medicine
Year:
2007

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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.