MommiNet: Mammographic Multi-view Mass Identification Networks

Abstract

Most Deep Neural Networks (DNNs) based approaches for mammogram analysis are based on single view. Some recent DNN-based multi-view approaches can perform either bilateral or ipsilateral analysis, while in practice, radiologists use both to achieve the best clinical outcome. In this paper, we present the first DNN-based tri-view mass identification approach (MommiNet), which can simultaneously perform end-to-end bilateral and ipsilateral analysis of mammogram images, and in turn can fully emulate the radiologists’ reading practice. Novel network architectures are proposed to learn the symmetry and geometry constraints, to fully aggregate the information from all views. Extensive experiments have been conducted on the public DDSM dataset and our in-house dataset, and state-of-the-art (SOTA) results have been obtained in terms of mammogram mass detection accuracy.