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DDR - Diabetic Retinopathy Detection Dataset
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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 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
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.