A Study on Generation of Retinal OCT Images Using Diffusion Models

Abstract

Recently, the application of AI technology in the clinical field has advanced significantly. For instance, some diagnostic algorithms in ophthalmology can diagnose cases as accurately as specialists. However, constructing such algorithms requires plenty of high-quality data. In this study, the authors proposed a data augmentation method for retinal OCT images and discussed the effect of added generated images on classification performance. The experimental results indicated that classification accuracy was maintained when the generated images were appropriately proportioned. We confirmed that the proposed method is useful as a data augmentation method for the disease classification of OCT images. Our approach is expected to enable the construction of highly accurate disease classification algorithms with limited datasets.