We evaluated whether using synthetic mammograms for training data augmentation may reduce the effects of overfitting and increase the performance of a deep learning algorithm for breast mass detection. Synthetic mammograms were generated using a combination of an in-silico random breast generation algorithm and x-ray transport simulation. In-silico breast phantoms containing masses were modeled across the four BI-RADS breast density categories, and the masses were modeled with different sizes, shapes and margins. A Monte Carlo-based xray transport simulation code, MC-GPU, was used to project the 3D phantoms into realistic synthetic mammograms. A training data set of 2,000 mammograms with 2,522 masses were generated and used for augmenting a data set of real mammograms for training. The data set of real mammograms included all the masses in the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) and consisted of 1,112 mammograms (1,198 masses) for training, 120 mammograms (120 masses) for validation, and 361 mammograms (378 masses) for testing. We used Faster R-CNN for our deep learning network with pre-training from ImageNet using Resnet-101 architecture. We compared the detection performance when the network was trained using only the CBIS-DDSM training images, and when subsets of the training set were augmented with 250, 500, 1,000 and 2,000 synthetic mammograms. FROC analysis was performed to compare performances with and without the synthetic mammograms. Our study showed that enlarging the training data with synthetic mammograms shows promise in reducing the overfitting, and that the inclusion of the synthetic images for training increased the performance of the deep learning algorithm for mass detection on mammograms.