Title: Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networks,
Abstract: Data diversity is critical to success when training deep learning models.
Medical imaging data sets are often imbalanced as pathologic findings are
generally rare, which introduces significant challenges when training deep
learning models. In this work, we propose a method to generate synthetic
abnormal MRI images with brain tumors by training a generative adversarial
network using two publicly available data sets of brain MRI. We demonstrate two
unique benefits that the synthetic images provide. First, we illustrate
improved performance on tumor segmentation by leveraging the synthetic images
as a form of data augmentation. Second, we demonstrate the value of generative
models as an anonymization tool, achieving comparable tumor segmentation
results when trained on the synthetic data versus when trained on real subject
data. Together, these results offer a potential solution to two of the largest
challenges facing machine learning in medical imaging, namely the small
incidence of pathological findings, and the restrictions around sharing of
patient data.