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Aljo-na/DDR-dataset

DDR - Diabetic Retinopathy Detection Dataset Image: Dataset Samples. The DDR (Diabetic Retinopathy Detection) dataset is a large-scale collection of retinal fundus images designed for training and evaluating algorithms in diabetic retinopathy (DR) grading and lesion-level segmentation. It provides both image-level DR labels and pixel-level annotations of pathological features, making it suitable for… See the full description on the dataset page: https://huggingface.co/datasets/Aljo-na/DDR-dataset.

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Dataset Card

DDR - Diabetic Retinopathy Detection Dataset

<table align="center"> <tr> <td width="100%" align="center"> <img src="rmimages/MergedFundusImageswith_Captions.jpg" alt="Merged Dataset Samples" style="max-width: 100%; height: auto;"> <br> <p><strong>Image:</strong> Dataset Samples.</p> </td> </tr> </table>

The DDR (Diabetic Retinopathy Detection) dataset is a large-scale collection of retinal fundus images designed for training and evaluating algorithms in diabetic retinopathy (DR) grading and lesion-level segmentation. It provides both image-level DR labels and pixel-level annotations of pathological features, making it suitable for classification and segmentation tasks.

Dataset Overview

—Full Name: Diabetic Retinopathy Detection and Segmentation Dataset (DDR)
—Authors: Yuhao Zhang, Mingxia Liu, Qianni Zhang, et al.
—Associated Paper: Diabetic Retinopathy Lesion Segmentation Method Based on Multi-Scale Attention and Lesion Perception Published in Information Sciences, Volume 501, 2019. ScienceDirect Link
—Source: Kaggle - DDR Dataset
—Institution: Chinese Academy of Sciences, Beijing, China
—License: CC BY 4.0

Dataset Structure

🧩 Categories

The dataset includes five DR severity levels, labeled according to the International Clinical Diabetic Retinopathy (ICDR) scale:

Label	Description
0	No Diabetic Retinopathy
1	Mild Nonproliferative DR
2	Moderate Nonproliferative DR
3	Severe Nonproliferative DR
4	Proliferative DR

Additionally, lesion masks are provided for:

—Microaneurysms
—Hemorrhages
—Hard exudates
—Soft exudates

Data Summary

Split	# Images	Annotation Type	Image Resolution
Train	~9,000	Image-level + lesion masks	3216×2136 px (avg.)
Test	~1,000	Image-level + lesion masks	3216×2136 px (avg.)

Total: ~10,000 color fundus images collected from multiple clinical sites in China.

Applications

—Diabetic Retinopathy Classification
—Lesion Segmentation and Detection
—Multi-scale Attention and Lesion-Aware Learning
—Retinal Disease Screening Benchmarking

Example Usage

python

from datasets import load_dataset

dataset = load_dataset("your-username/ddr-dataset")
example = dataset["train"][0]
image = example["image"]
mask = example["segmentation_mask"]

Citation

If you use this dataset, please cite:

Zhang Y, Liu M, Zhang Q, et al. Diabetic Retinopathy Lesion Segmentation Method Based on Multi-Scale Attention and Lesion Perception. Information Sciences, 2019; 501: 511–522. DOI: 10.1016/j.ins.2019.06.016

Acknowledgements

This dataset was originally collected and published by the Chinese Academy of Sciences and released for research use under a CC BY 4.0 license. Kaggle rehosting by Mariah Herrero.