Exploratory Learning with Convolutional Autoencoder for Discrimination of Architectural Distortion in Digital Mammography

Abstract

This work presents a deep learning approach based on autoencoder to improve the detection of architectural distortion (AD) in digital mammography. AD can be the earliest sign of breast cancer, appearing before the formation of any mass or calcification. However, it is very difficult to be detected and almost 50% of the cases are missed by the radiologists. Thus, we designed an autoencoder, based on a convolutional neural network (CNN), to work as a feature descriptor in a computer-aided detection (CAD) pipeline with the objective of detecting AD in digital mammography. This model was trained with 140,000 regions-of-interest (ROI) extracted from clinical mammograms. These samples were divided in two groups, with and without AD, according to the radiologist's report. Validation was done comparing the classifier performance when using the proposed autoencoder and other well-known feature descriptors, commonly used for the task of detecting AD in digital mammograms. The results showed that the performance of the autoencoder is slightly higher than that of other descriptors. However, the complexity and the computational cost of the autoencoder is much higher when compared to the hand-crafted descriptors.