Improving the automated detection of calcifications by combining deep cascades and deep convolutional nets

Recently, both Deep Cascade classifiers and Convolutional Neural Networks (CNNs) have achieved state-ofthe-art microcalcification (MC) detection performance in digital mammography. Deep Cascades consist in long sequences of weak classifiers designed to effectively learn from heavily unbalanced data as in the case of MCs (~ 1 MC every 10, 000 non-MC samples). CNNs are powerful models that achieve impressive results for image classification thanks to the ability to automatically extract general-purpose features from the data, but require balanced classes. In this work, we introduce a two-stage classification scheme that combines the benefits of both systems. Firstly, Deep Cascades are trained by requiring a very high sensitivity (99.5%) throughout the sequence of classifiers. As a result, while the number of MC samples remains practically unchanged, the number of non-MC samples is greatly reduced. The remaining data, approximately balanced, are used to train an additional stage of classification with a CNN. We evaluated the proposed approach on a database of 1, 066 digital mammograms. MC detection results of the combined classification were statistically significantly higher than Deep Cascade and CNN alone, yielding an average improvement in mean sensitivity of 3.19% and 2.45%, respectively. Remarkably, the proposed system also yielded a faster per-mammogram processing time (2.0s) compared to Deep Cascade (2.5s) and CNN (5.7s).