Lelihu/DR_Artifacts

---
license: mit
task_categories:
- image-classification
- image-segmentation
- image-feature-extraction
language:
- en
tags:
- medical
---

# DDR-Augmented-Artifacts

## Dataset Summary

**DDR-Augmented-Artifacts** provides fundus images augmented with realistic synthetic artifacts.
Artifacts were cropped from anonymized retina images showing reflections from blood vessels, segmented, and overlaid on DDR images using Gaussian feathered masks and Poisson blending.

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## Example

Below is a sample visualization of how the dataset looks:

| Original DDR Image | Augmented with Artifact |
|--------------------|--------------------------|
| <img src="https://huggingface.co/datasets/shubham212/DR_Artifacts/resolve/main/images/original.jpg" width="250"/> | <img src="https://huggingface.co/datasets/shubham212/DR_Artifacts/resolve/main/images/augmented.png" width="250"/> |

---

## Code Repository

The dataset is accompanied by code for artifact generation, preprocessing, and training, available at:

👉 [GitHub Repository](https://github.com/Shubham2376G/DR_Artifacts)

This repository contains:
- Scripts for generating synthetic artifacts
- Example U-Net model for artifact removal

## Supported Tasks

- **Image Classification:** Train and evaluate DR classifiers on artifact-rich data.
- **Image Segmentation:** Evaluate lesion/DR segmentation robustness under artifacts.
- **Preprocessing / Artifact Removal:** Train models to **identify and remove imaging artifacts** prior to downstream analysis.

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## Languages

- Image-based dataset (no natural language component).

---

## Dataset Structure

### Data Fields
- `image_id`: filename of the image (string)
- `severity_level`: integer label (0–4) indicating DR severity
- **0** = No DR
- **1** = Mild
- **2** = Moderate
- **3** = Severe
- **4** = Proliferative DR

### Data Splits
The dataset is provided as a flat collection of images with a CSV label file.
Users should generate **train/val/test splits** at the **patient level** to prevent data leakage.

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## Intended Uses

- Research on **artifact robustness** in medical imaging AI.
- Developing augmentation pipelines for retinal image datasets.
- Training preprocessing modules to **detect and remove acquisition artifacts**.

---

## Limitations

- Synthetic artifacts may not capture full variability of real-world imaging conditions.
- Not intended for clinical use.

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## Ethics and Privacy

- All patches were derived from fully anonymized personal retinal images.
- No patient-identifiable data is present.
- Dataset is for research purposes only.

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## Citation

Please cite both the DDR dataset and this work:

```bibtex
@article{LI2019,
title = "Diagnostic Assessment of Deep Learning Algorithms for Diabetic Retinopathy Screening",
author = "Tao Li and Yingqi Gao and Kai Wang and Song Guo and Hanruo Liu and Hong Kang",
journal = "Information Sciences",
volume = "501",
pages = "511 - 522",
year = "2019",
issn = "0020-0255",
doi = "https://doi.org/10.1016/j.ins.2019.06.011",
url = "http://www.sciencedirect.com/science/article/pii/S0020025519305377",
}

@misc{Aggarwal2025_arxiv,
title = DDR-Augmented-Artifacts: Synthetic Artifact Overlays for Robust Diabetic Retinopathy Models,
author = Shubham Aggarwal,
year = "2025",
url = https://arxiv.org/abs/XXXX.XXXXX
}