Causal Probabilistic Modelling for Two-View Mammographic Analysis

Abstract

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.

Thirty years of artificial intelligence in medicine (AIME) conferences: A review of research themes

“…AIME research has increasingly focused on exploiting Bayesian networks for specific biological or clinical problems, such as mammographic image interpretation [116] and risk factor interactions in multimorbid patients [117].…”