Applications of Multi-Resolution Neural Networks to Mammography

Abstract

We have previously presented a coarse-to-fine hierarchical pyra(cid:173) mid/neural network (HPNN) architecture which combines multi(cid:173) scale image processing techniques with neural networks. In this paper we present applications of this general architecture to two problems in mammographic Computer-Aided Diagnosis (CAD). The first application is the detection of microcalcifications. The <:oarse-to-fine HPNN was designed to learn large-scale context in(cid:173) formation for detecting small objects like microcalcifications. Re(cid:173) ceiver operating characteristic (ROC) analysis suggests that the hierarchical architecture improves detection performance of a well established CAD system by roughly 50 %. The second application is to detect mammographic masses directly. Since masses are large, extended objects, the coarse-to-fine HPNN architecture is not suit(cid:173) able for this problem. Instead we construct a fine-to-coarse HPNN architecture which is designed to learn small-scale detail structure associated with the extended objects. Our initial results applying the fine-to-coarse HPNN to mass detection are encouraging, with detection performance improvements of about 36 %. We conclude that the ability of the HPNN architecture to integrate information across scales, both coarse-to-fine and fine-to-coarse, makes it well suited for detecting objects which may have contextual clues or detail structure occurring at scales other than the natural scale of the object.