Early experience of adopting a generative diffusion model for the synthesis of fundus photographs
Description
This study was based on a publicly accessible and deidentified FP image database, which was released by a previous study. To reduce the noise of different borders, we selected FPs where all boundaries of the circular mask were intact. Finally, 1000 FPs were used to train the diffusion model. Therefore, this study was exempt from ethical review according to the guidelines by Korea National Institute for Bioethics Policy. All the procedures were performed in accordance with the ethical standards of the 1964 Declaration of Helsinki and its later amendments. As shown in Figure 1, we trained the DDPM based on U-Net backbone architecture, which is the most popular form of generative diffusion model. After training, serial multiple denoising U-Nets can generate FPs using random noise seeds. The input image size was set to a pixel resolution of 128 × 128. This was the maximum resolution required to finalize the proper training with our computational resources. We set the time step to the default value of 1000 because fewer numbers produce severely noisy or blurred synthetic images. All codes for the implementation of the DDPM are available on the webpage (https://github.com/lucidrains/denoising-diffusion-pytorch). For reproducibility, our FP images and modified codes, which can be implemented in Google Colaboratory, were released in the data repository.