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.

综合临床专业知识和深度学习技术，通过利用扩散模型生成的合成图像和真实图像相结合，文中通过基于 NAFLD 分类的实验表明，综合使用效果明显优于仅使用生成对抗网络（GANs）进行图像增广，达到了 0.904 的最佳图像级别 ROC AUC 性能。

利用潜在扩散模型提高非酒精性脂肪肝疾病分类性能