Causal Probabilistic Modelling for Two-View Mammographic Analysis

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 brea...

• Type: Conference
• Year: 2009
• Where: AIME
• Authors: Marina Velikova, Maurice Samulski, Peter J. F. Lucas, Nico Karssemeijer
• Keywords: Actual Mammographic Data, AIME 2009, Artificial Intelligence, Bayesian Networks—causal Independence, Mammographic Analysis