  Acquiring large quantities of data and annotations is known to be effective
for developing high-performing deep learning models, but is difficult and
expensive to do in the healthcare context. Adding synthetic training data using
generative models offers a low-cost method to deal effectively with the data
scarcity challenge, and can also address data imbalance and patient privacy
issues. In this study, we propose a comprehensive framework that fits
seamlessly into model development workflows for medical image analysis. We
demonstrate, with datasets of varying size, (i) the benefits of generative
models as a data augmentation method; (ii) how adversarial methods can protect
patient privacy via data substitution; (iii) novel performance metrics for
these use cases by testing models on real holdout data. We show that training
with both synthetic and real data outperforms training with real data alone,
and that models trained solely with synthetic data approach their real-only
counterparts. Code is available at
https://github.com/Global-Health-Labs/US-DCGAN.
