A preliminary study on breast cancer risk analysis using deep neural network

Abstract

Deep learning is a powerful tool in computer vision areas, but it is most effective when applied to large training sets. However, large dataset are not always available for medical images. In this study we proposed a new method to use deep neural network for near-term breast cancer risk analysis. In our data base, we have 420 cases with two sequential mammogram screenings, and half of the cases were diagnosed as positive in the second screening and the other half remained negative. Instead of using human designed features, we designed a deep neural network (DNN) with four pairs of convolution neural network and one fully connected layer. Every breast image were divided into 100 ROIs with 52 by 52 pixels, and each ROI were trained with the DNN individually, and the final predictions of each case were based on the overall risk scores of all the 100 ROIs. And the ROI based area under the curve (AUC) is 0.6982, and the case based AUC is 0.7173 using our proposed scheme. The results showed our proposed scheme is promising to apply deep learning algorithms in predicting near-term breast cancer risk with limited data size.