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

Abstract 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. The dataset, augmentation scripts, and a sample demonstration model are publicly available at: Competing Interest Statement The authors have declared no competing interest. Clinical Protocols https://github.com/Shubham2376G/DR_Artifacts https://huggingface.co/datasets/shubham212/DR_Artifacts Funding Statement This study did not receive any funding Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The study used only openly available, de-identified human retinal images from the DDR dataset, originally located at: https://github.com/nkicsl/DDR-dataset I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data Availability The dataset, augmentation scripts, and a sample demonstration model are publicly available at: GitHub: https://github.com/Shubham2376G/DR_Artifacts Hugging Face: https://huggingface.co/datasets/shubham212/DR_Artifacts The original DDR dataset used in this study is publicly available at https://github.com/nkicsl/DDR-dataset https://github.com/nkicsl/DDR-dataset