Abstract
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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Velikova, M., Samulski, M., Lucas, P.J.F., Karssemeijer, N. (2009). Causal Probabilistic Modelling for Two-View Mammographic Analysis. In: Combi, C., Shahar, Y., Abu-Hanna, A. (eds) Artificial Intelligence in Medicine. AIME 2009. Lecture Notes in Computer Science(), vol 5651. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02976-9_56
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DOI: https://doi.org/10.1007/978-3-642-02976-9_56
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