Improving Nonalcoholic Fatty Liver Disease Classification Performance With Latent Diffusion Models

TL;DR

This paper shows that using synthetic images generated by diffusion models can improve the accuracy of NAFLD classification, especially when limited real data is available.

Contribution

It introduces a novel approach combining diffusion-generated synthetic images with real data to enhance NAFLD classification performance.

Findings

Diffusion-generated images outperform GANs in quality metrics (IS and FID).

Synthetic augmentation improves ROC AUC to 0.904 in NAFLD classification.

Diffusion models are effective for medical image data augmentation.

Abstract

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 even in low-data regime settings. 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- and generative adversarial network (GAN)-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 $100.05$ 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.