Cascaded Rank-Based Classifiers for Detecting Clusters of Microcalcifications

A Computer Aided Detection (CAD) system has frequently to deal with a significant skew between positive and negative class. For this reason we propose a solution based on an ensemble of classifiers structured as a “cascade” of dichotomizers where each node is robust to such skew since it is trained by a learning algorithm based on ranking instead of classification error. The proposed approach has been applied to the detection of clusters of microcalcifications in mammograms and has shown good performance in comparison with other methods well suited to deal with unbalanced problems.