A diffusion model-based self-explainable generative classifier for retinal image analysis

Vision-influencing ocular conditions, such as diabetic retinopathy (DR), age-related macular degeneration (AMD), cataracts, glaucoma, and degenerative myopia, affect millions of patients globally. Early diagnosis and treatment can reduce the impact of those conditions, which can be identified in color fundus photographs (CFPs). However, manual interpretation of CFPs is a time-consuming process that requires visual assessment by an experienced ophthalmologist. Therefore, several deep learning models have been proposed to support automated retinal disease detection / classification from CFPs. While those discriminative classification models usually achieve a high performance, their black-box nature generally creates challenges when trying to understand their decision-making process, which is crucial to establish trust in their results in a clinical setup. To alleviate this problem, this paper introduces the first fully self-explainable generative classifier for the computer-aided diagnosis of multiple ocular conditions from CFPs. More precisely, we propose a generative diffusion model, which we then repurpose as a classifier. In contrast to discriminative models, our generative classifier is inherently self-explainable due to its ability to synthesize so-called counterfactual images that can be used to visually highlight alternate classification outcomes. We train and evaluate the proposed method on more than 25,000 images encompassing five different ocular conditions, including age-related macular degeneration and diabetic retinopathy, and achieve a competitive classification accuracy of 97.38%. Moreover, the counterfactual images generated by our model visually confirm that it has learned the hallmark features of the ocular conditions included in the data. We believe that our self-explainable generative classifier is an important step towards trustworthy AI in ophthalmology as it removes the explainability challenges surrounding standard discriminative classifiers.