Breast cancer detection using synthetic mammograms from generative adversarial networks in convolutional neural networks

Abstract

The Convolutional Neural Network (CNN) is a promising technique to detect breast cancer based on mammograms. Training the CNN from scratch, however, requires a large amount of labeled data. Such a requirement usually is infeasible for some kinds of medical image data such as mammographic tumor images. Because improvement of the performance of a CNN classifier requires more training data, the creation of new training images -- image augmentation – could be one solution to this problem. In this study, we applied the Generative Adversarial Network (GAN) to generate synthetic mammographic images from the Digital Database for Screening Mammography (DDSM). From the DDSM, we cropped two sets of regions of interest (ROIs) from the images: normal and abnormal (cancer/tumor) Those ROIs were used to train the GAN, and the GAN then generated synthetic images. To compare the GAN with the affine transformation augmentation methods, such as rotation, shifting, scaling, etc., we used six groups of ROIs (three simple groups: affine augmented, GAN synthetic, real (original), and three mixture groups of each pair of the three simple groups) for each to train a CNN classifier from scratch. And, we used real ROIs that were not used in training to validate classification outcomes. Our results show that, to classify the normal ROIs and abnormal (tumor) ROIs from DDSM, adding GAN-generated ROIs to the training data can reduce overfitting of the classifier. But the affine transformations performed slightly better than GAN. Therefore, GAN could be an optional augmentation approach. The images augmented by GAN or affine transformation cannot substitute entirely for real images to train CNN classifiers because the absence of real images in the training set will cause serious over-fitting with more training.