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.

翻译：获取大量数据与标注被证实是开发高性能深度学习模型的有效途径，但在医疗场景中这一过程既困难又昂贵。利用生成模型添加合成训练数据，为有效应对数据稀缺挑战提供了一种低成本方法，同时还能解决数据不平衡和患者隐私问题。本研究提出了一套能够无缝融入医学图像分析模型开发流程的综合性框架。我们通过不同规模的数据集展示了：（i）生成模型作为数据增强方法的优势；（ii）对抗性方法如何通过数据替换保护患者隐私；（iii）基于真实留出数据测试模型得到的新性能评估指标。研究表明，使用合成数据与真实数据联合训练的效果优于仅使用真实数据，而仅使用合成数据训练的模型性能已接近仅使用真实数据的模型。代码已开源在 https://github.com/Global-Health-Labs/US-DCGAN。