EDM: Synthetic Data from Exemplar Diffusion Model Improves Non-Communicable Diseases Detection

Abstract

There have been researches revealing obvious associations between facial phenotypes and non-communicable diseases (NCDs), which enables effective health assessment with the integration of model-based learning methods. However, the paucity and poor quality of available datasets hinder the development of potent algorithms to detect NCDs. To meet this challenge, we propose a method called Exemplar Diffusion Model (EDM), the objective of proposed EDM is to generate facial images that illustrate simulated non-communicable diseases, utilizing a normal facial image as input. Extensive experimental results show that the proposed EDM method outperforms the state-of-the-art methods in terms of Frechet Inception Distance (FID) and Quality Score (QS), with improvements of 0.11 and 0.74, respectively. Furthermore, comprehensive ablation studies and comparative experiments prove the value of proposed EDM method in large-scale facial image dataset generation and non-communicable disease detection.