Relational representation for improved decisions with an information-theoretic CADe system: Initial experience

Our previously presented information-theoretic computer-aided detection (IT-CADe) system for distinguishing masses and normal parenchyma in mammograms is an example of a case-based system. IT-CAD makes decisions by evaluating the querys average similarity with known mass and normal examples stored in the systems case base. Pairwise case similarity is measured in terms of their normalized mutual information. The purpose of this study was to evaluate whether incorporating a new machine learning concept of relational representation to IT-CAD is a more effective strategy than the decision algorithm that is currently in place. A trainable relational representation classifier builds a decision rule using the relational representation of cases. Instead of describing a case by a vector of intrinsic features, the case is described by its NMI-based similarity to a set of known examples. For this study, we first applied random mutation hill climbing algorithm to select the concise set of knowledge cases and then we applied a support vector machine to derive a decision rule using the relational representation of cases. We performed the study with a database of 600 mammographic regions of interest (300 with masses and 300 with normal parenchyma). Our experiments indicate that incorporating the concept of relational representation with a trainable classifier to IT-CAD provides an improvement in performance as compared with the original decision rule. Therefore, relational representation is a promising strategy for IT-CADe.