System and method for joint optimization of cascaded classifiers for computer aided detection

Abstract

A method for training classifiers for Computer-Aided Detection in medical images includes providing an image feature training set {(x, y)}, wherein xεRare input feature variables and yε{−1,1} are class labels, and a cascade of K classifiers to be trained, minimizing, for each classifier k, a first cost function to initialize an αassociated with each classifier k, fixing all classifiers except classifier k and minimizing a second cost function to solve for αfor a counter value c using the training dataset {(x, y)}, calculating a third cost function J(α, . . . , α) for each classifier k, and comparing Jwith a previous iteration J, wherein if J−Jis less than a predetermined tolerance, said classifier training is completed.