Capturing contextual dependencies in medical imagery using hierarchical multi-scale models

In this paper we summarize our results for two classes of hierarchical multi-scale models that exploit contextual information for detection of structure in mammographic imagery. The first model, the hierarchical pyramid neural network (HPNN), is a discriminative model which is capable of integrating information either coarse-to-fine or fine-to-coarse for microcalcification and mass detection. The second model, the hierarchical image probability (HIP) model, captures short-range and contextual dependencies through a combination of coarse-to-fine factoring and a set of hidden variables. The HIP model, being a generative model, has broad utility, and we present results for classification, synthesis and compression of mammographic mass images. The two models demonstrate the utility of the hierarchical multi-scale framework for computer assisted detection and diagnosis.