Reliable automatic diagnosis of Diabetic Retinopathy (DR) and Macular Edema (ME) is an invaluable asset in improving the rate of monitored patients among at-risk populations and in enabling earlier treatments before the pathology progresses and threatens vision. However, the explainability of screening models is still an open question, and specifically designed datasets are required to support the research. We present MAPLES-DR (MESSIDOR Anatomical and Pathological Labels for Explainable Screening of Diabetic Retinopathy), which contains, for 198 images of the MESSIDOR public fundus dataset, new diagnoses for DR and ME as well as new pixel-wise segmentation maps for 10 anatomical and pathological biomarkers related to DR. This paper documents the design choices and the annotation procedure that produced MAPLES-DR, discusses the interobserver variability and the overall quality of the annotations, and provides guidelines on using the dataset in a machine learning context.

翻译：糖尿病视网膜病变（DR）及黄斑水肿（ME）的可靠自动诊断对于提高高危人群监测覆盖率、在病理进展威胁视力前实现早期干预具有不可替代的价值。然而，筛查模型的可解释性仍是未解难题，亟需专门设计的标注数据集支撑相关研究。我们提出MAPLES-DR（MESSIDOR解剖与病理标注数据集，用于可解释糖尿病视网膜病变筛查），该数据集包含MESSIDOR公共眼底数据集中198幅图像的新版DR与ME诊断标注，以及10种与DR相关的解剖及病理生物标志物的像素级分割图。本文系统阐述了MAPLES-DR数据集的设计原则与标注流程，分析了标注者间差异性与整体标注质量，并提供了该数据集在机器学习场景下的使用指南。