Data Resources

Below is a curated list of datasets available for research in diabetic retinopathy and ophthalmological analysis. These datasets cover a wide range of retinal imaging challenges, including multilabel classification, lesion segmentation, and disease progression analysis.

• A Brazilian Multilabel Ophthalmological Dataset: A multilabel dataset containing retinal images with annotations for various ophthalmological conditions.
  • BRSET
• Messidor-1 & Messidor-2 Dataset: A widely used dataset for diabetic retinopathy detection and grading, containing fundus images.
  • Messidor Dataset
• RFMiD Dataset: A large-scale dataset for retinal fundus disease classification, with multiple annotated diseases.
  • RFMiD Dataset
• APOTS Dataset: A dataset focused on retinal optical tomography images, enabling the study of various retinal conditions.
  • APTOS-2019 Dataset
• IDRiD Dataset: A comprehensive dataset for diabetic retinopathy lesion segmentation, disease grading, and severity assessment.
  • IDRiD Dataset
• MICCAI 2023 MMAC (Myopic Maculopathy Analysis Challenge): A dataset from the MICCAI 2023 challenge focused on myopic maculopathy segmentation and classification.
  • MMAC Dataset
• EyePACS Dataset: A large-scale dataset used in the Kaggle diabetic retinopathy detection competition, containing fundus images with severity labels.
  • *EyePACS Dataset
• DRIVE Dataset (Digital Retinal Images for Vessel Extraction): A dataset focused on retinal vessel segmentation.
  • DRIVE Dataset
• STARE Dataset: A dataset containing images for retinal disease diagnosis and vessel segmentation.
  • STARE Dataset