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Acquiring large quantities of data and annotations is known to be effectivefor developing high-performing deep learning models, but is difficult andexpensive to do in the healthcare context. Adding synthetic training data usinggenerative models offers a low-cost method to deal effectively with the datascarcity challenge, and can also address data imbalance and patient privacyissues. In this study, we propose a comprehensive framework that fitsseamlessly into model development workflows for medical image analysis. Wedemonstrate, with datasets of varying size, (i) the benefits of generativemodels as a data augmentation method; (ii) how adversarial methods can protectpatient privacy via data substitution; (iii) novel performance metrics forthese use cases by testing models on real holdout data. We show that trainingwith both synthetic and real data outperforms training with real data alone,and that models trained solely with synthetic data approach their real-onlycounterparts. Code is available athttps://github.com/Global-Health-Labs/US-DCGAN.

How Good Are Synthetic Medical Images? An Empirical Study with Lung Ultrasound