Latent feature representation with 3-D multi-view deep convolutional neural network for bilateral analysis in digital breast tomosynthesis

Abstract

In clinical studies of breast cancer, masses appear as asymmetric densities between the left and the right breasts, which show different breast tissue structures. For classifying breast masses, most researchers have developed hand-crafted bilateral features by extracting the asymmetric information in 2-D mammograms. In digital breast tomosynthesis (DBT), which has 3D volume data, effective bilateral features are needed to detect masses. In this paper, we propose latent bilateral feature representation with 3-D multi-view deep convolutional neural network (DCNN) in the DBT reconstructed volume. The proposed DCNN is designed to discover hidden or latent bilateral feature representation of masses in self-taught learning. Experimental results show that the proposed latent bilateral feature representation outperforms conventional hand-crafted features by achieving a high area under the receiver operating characteristic curve.