Causal Probabilistic Modelling for Two-View Mammographic Analysis
Mammographic analysis is a difficult task due to the complexity of image interpretation. This results in diagnostic uncertainty, thus provoking the need for assistance by computer decision-making tools. Probabilistic modelling based on Bayesian networks is among the suitable tools, as it allows for the formalization of the uncertainty about parameters, models, and predictions in a statistical manner, yet such that available background knowledge about characteristics of the domain can be taken into account. In this paper, we investigate a specific class of Bayesian networks—causal independence models—for exploring the dependencies between two breast image views. The proposed method is based on a multi-stage scheme incorporating domain knowledge and information obtained from two computer-aided detection systems. The experiments with actual mammographic data demonstrate the potential of the proposed two-view probabilistic system for supporting radiologists in detecting breast cancer, both at a location and a patient level.
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- 2.Samulski, M., Karssemeijer, N.: Matching mammographic regions in mediolateral oblique and cranio caudal views: A probabilistic approach. In: Proceedings of SPIE, Medical Imaging, vol. 6915 (2008)Google Scholar
- 3.Good, W., Zheng, B., Chang, Y., Wang, X., Maitz, G., Gur, D.: Multi-image cad employing features derived from ipsilateral mammographic views. In: Proceedings of SPIE, Medical Imaging, vol. 3661 (1999)Google Scholar
- 7.Heckerman, D., Breese, J.S.: Causal independence for probability assessment and inference using Bayesian networks. IEEE Trans. on SMC–A 26, 826–831 (1996)Google Scholar
- 8.Visscher, S., Lucas, P.J.F., Schurink, C.A.M., Bonten, M.J.M.: Modelling treatment effects in a clinical Bayesian network using Boolean threshold functions. Artificial Intelligence in Medicine (2008)Google Scholar
- 9.Murphy, K.: Bayesian Network Toolbox (BNT) (2007), http://www.cs.ubc.ca/~murphyk/Software/BNT/bnt.html