1. A computer-implemented method for classifying a structure in digital image data, comprising: (a) extracting a first set of features from the digital image data; (b) learning a discriminative model based on the first set of features, wherein the discriminative model is associated with at least one conditional probability of a class label given an image data observation; (c) determining, based on the at least one conditional probability, at least one likelihood measure of the structure co-occurring with another structure in a same sub-volume of the digital image data; and (d) extracting a second set of features from the one or more likelihood measures.