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.
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Burnside, E.S., Rubin, D.L., Fine, J.P., Shachter, R.D., Sisney, G.A., Leung, W.K.: Bayesian network to predict breast cancer risk of mammographic microcalcifications and reduce number of benign biopsy results. Journal of Radiology (2006)
Kahn, C.E., Roberts, L.M., Shaffer, K.A., Haddawy, P.: Construction of a bayesian network for mammographic diagnosis of breast cancer. Computers and Biology and Medicine 27(1), 19–30 (1997)
Boutilier, C., Friedman, N., Goldszmidt, M., Koller, D.: Context-specific independence in bayesian networks. In: Proc. of the 12th UAI Conference (1998)
Geiger, D., Heckerman, D.: Knowledge representation and inference in similarity networks and bayesian multinets. Artificial Intelligence 82, 45–74 (1996)
Heckerman, D., Breese, J.S.: A new look at causal independence. In: Proc. of the 10th UAI Conference, pp. 286–292 (1994)
Wellman, M.P., Breese, J.S., Goldman, R.P.: From knowledge bases to decision models. The Knowledge Engineering Review 7(1), 35–53 (1992)
BI-RADS: Breast Imaging Reporting and Data System (BI-RADS). American College of Radiology, Reston, VA (1993)
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Velikova, M., de Carvalho Ferreira, N., Lucas, P. (2007). Bayesian Network Decomposition for Modeling Breast Cancer Detection. In: Bellazzi, R., Abu-Hanna, A., Hunter, J. (eds) Artificial Intelligence in Medicine. AIME 2007. Lecture Notes in Computer Science(), vol 4594. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73599-1_47
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DOI: https://doi.org/10.1007/978-3-540-73599-1_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73598-4
Online ISBN: 978-3-540-73599-1
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