Method for Disclosing the Reasoning Behind Computer-Aided Diagnosis of Pulmonary Nodules

Abstract

This paper proposes a method for disclosing the reasoning behind computer-aided diagnosis (CADx) based on a Bayesian network. The purpose of this method is to promote the acceptance of CADx by physicians by providing the reasoning behind the inferences. The proposed method first calculates the influence ratio to the inference result for each subset of input information. It then selects some subsets that have large influence ratios and shows them as the reasoning or grounds for the inference. In experiments using artificial data with known classification rules, the proposed method detected correct rules for about 90% of the data. With regard to clinical data, the average value for the effectiveness of reasoning as judged by two physicians was 3.4. This value is greater than “3,” which is considered a reasonable grade.