Interpretable Retinal Disease Prediction Using Biology-Informed Heterogeneous Graph Representations

--Interpretability is crucial to enhance trust in machine learning models for medical diagnostics. However, most state-of-the-art image classifiers based on neural networks are not interpretable. As a result, clinicians often resort to known biomarkers for diagnosis, although biomarker-based classification typically performs worse than large neural networks. This work proposes a method that surpasses the performance of established machine learning models while simultaneously improving prediction interpretability for diabetic retinopathy staging from optical coherence tomography angiography (OCT A) images. Our method is based on a novel biology-informed heterogeneous graph representation that models retinal vessel segments, intercapillary areas, and the foveal avascular zone (F AZ) in a human-interpretable way. This graph representation allows us to frame diabetic retinopathy staging as a graph-level classification task, which we solve using an efficient graph neural network. Our model outperforms all baselines on two datasets. Crucially, we use our biology-informed graph to provide explanations of unprecedented detail. In addition, we give informative and human-interpretable attributions to critical characteristics. Our work contributes to the development of clinical decision-support tools in ophthalmology. Diabetic Retinopathy (DR), a complication of diabetes that affects the retinal vasculature, is one of the leading causes of blindness in adulthood [1]. It is associated with pathological changes to the retinal microvasculature, resulting in a widening of the intercapillary areas, and enlargement of the foveal avascular zone (FAZ). Currently, clinicians study biomarkers that capture these changes, such as blood vessel density (BVD), Fractal Dimension (FD), and FAZ area.