  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.
