DDR-Augmented-Artifacts: Synthetic Artifact Overlays for Robust Diabetic Retinopathy Models

Deep learning models for the screening of diabetic retinopathy (DR) have achieved near- human performance on benchmark datasets, but their performance deteriorates in real-world settings due to imaging artifacts such as glare, blur, and reflections. Current public datasets such as DDR contain high-quality fundus images, but they lack the variability and imperfections seen in handheld fundus photography. This mismatch results in models that fail in practice, particularly in low-resource environments where handheld cameras are widely deployed. We introduce DDR-Augmented-Artifacts, an artifact-augmented extension of the DDR dataset that simulates realistic reflection artifacts via patch-based Poisson blending. Unlike prior datasets that exclude noisy images, our dataset explicitly models these challenges, allowing researchers to benchmark and train models that are robust to real-world noise.