Integrating deep learning with clinical expertise holds great potential for addressing healthcare challenges and empowering medical professionals with improved diagnostic tools. However, the need for annotated medical images is often an obstacle to leveraging the full power of machine learning models. Our research demonstrates that by combining synthetic images, generated using diffusion models, with real images, we can enhance nonalcoholic fatty liver disease (NAFLD) classification performance. We evaluate the quality of the synthetic images by comparing two metrics: Inception Score (IS) and Fr\'{e}chet Inception Distance (FID), computed on diffusion-generated images and generative adversarial networks (GANs)-generated images. Our results show superior performance for the diffusion-generated images, with a maximum IS score of $1.90$ compared to $1.67$ for GANs, and a minimum FID score of $69.45$ compared to $99.53$ for GANs. Utilizing a partially frozen CNN backbone (EfficientNet v1), our synthetic augmentation method achieves a maximum image-level ROC AUC of $0.904$ on a NAFLD prediction task.

翻译：将深度学习与临床专业知识相结合，在应对医疗保健挑战和提升医学诊断工具方面具有巨大潜力。然而，标注医学图像的需求常常阻碍机器学习模型充分发挥其能力。本研究证明，通过将扩散模型生成的合成图像与真实图像相结合，能够提升非酒精性脂肪肝病（NAFLD）的分类性能。我们通过比较两个指标评估合成图像的质量：在扩散模型生成的图像和生成对抗网络（GANs）生成的图像上分别计算Inception Score（IS）和Fr´echet Inception Distance（FID）。结果表明，扩散模型生成的图像性能更优，其最高IS得分为$1.90$（GANs为$1.67$），最低FID得分为$69.45$（GANs为$99.53$）。利用部分冻结的CNN主干网络（EfficientNet v1），我们的合成增强方法在NAFLD预测任务中实现了最高图像级ROC AUC为$0.904$。