Abstract
By incorporating multiple indicators that facilitate clinical decision making and effective management of diabetic retinopathy (DR), a comprehensive understanding of the progression of the disease can be achieved. However, the diversity of DR complications poses challenges to the automatic analysis of various information within images. This study aims to establish a deep learning system designed to examine various metrics linked to DR in ultra-widefield fluorescein angiography (UWFA) images. We have developed a unified model based on image generation that transforms input images into corresponding disease-free versions. By incorporating an image-level supervised training process, the model significantly reduces the need for extensive manual involvement in clinical applications. Furthermore, compared to other comparative methods, the quality of our generated images is significantly superior.