1. A method for computer-assisted detection (CAD) of regions or volumes of interest (“regions”) within medical image data that includes CAD processing to detect and delineate candidate regions, and post-CAD machine learning in a training phase to maximize specificity and reduce the number of false positives reported after processing non-training data, which method includes the steps of: training a classifier on a set of medical image training data selected to include a number of regions known to be true and false for a ground truth, identifying and segmenting the regions using said CAD processing, extracting features to create a pool of features to qualify the regions, including at least one of a 3D histogram-based feature, and a 3D gradient-based feature, applying a genetic algorithmic processor to the pool of features to determine a minimal sub-set of features for use by a support vector machine (SVM) to identify candidate regions within non-training data with improved specificity; detecting, within non-training data, candidate regions segmenting the candidate regions within the non-training data; extracting a set of features relating to each segmented candidate region; and mapping candidate regions by the SVM using the sets of features.