Tree RE-weighted belief propagation using deep learning potentials for mass segmentation from mammograms

Abstract

In this paper, we propose a new method for the segmentation of breast masses from mammograms using a conditional random field (CRF) model that combines several types of potential functions, including one that classifies image regions using deep learning. The inference method used in this model is the tree re-weighted (TRW) belief propagation, which allows a learning mechanism that directly minimizes the mass segmentation error and an inference approach that produces an optimal result under the approximations of the TRW formulation. We show that the use of these inference and learning mechanisms and the deep learning potential functions provides gains in terms of accuracy and efficiency in comparison with the current state of the art using the publicly available datasets INbreast and DDSM-BCRP.