A feasibility study on the adoption of a generative denoising diffusion model for the synthesis of fundus photographs using a small dataset

Abstract The generative diffusion model has been highlighted as a state-of-the-art artificial intelligence technique for image synthesis. Here, we show that a denoising diffusion probabilistic model (DDPM) can be used for a domain-specific task generating fundus photographs based on a limited training dataset in an unconditional manner. We trained the DDPM based on U-Net backbone architecture, which is the most popular form of the generative diffusion model. After training, serial multiple denoising U-Nets can generate FPs using random noise seeds. A thousand healthy retinal images were used to train the diffusion model. The input image size was set to a pixel resolution of 128 × 128. The trained DDPM successfully generated synthetic fundus photographs with a resolution of 128 × 128 pixels using our small dataset. We failed to train the DDPM for 256-by-256-pixel images due to the limited computation capacity using a personal cloud platform. In a comparative analysis, the progressive growing generative adversarial network (PGGAN) model synthesized more sharpened images than the DDPM in the retinal vessels and optic discs. The PGGAN (Frechet inception distance FID score: 41.761) achieved a better FID score than the DDPM (FID score: 65.605). We used a domain-specific generative diffusion model to synthesize fundus photographs based on a relatively small dataset. Because the DDPM has disadvantages with a small dataset, including difficulty in training and low image quality compared with generative adversarial networks such as PGGAN, further studies are needed to improve diffusion models for domain-specific medical tasks with small numbers of samples.