Update README.md

README.md

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----
-license: mit
----
+---
+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.
+
+---
+
+## 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.  
+
+---
+
+## 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.  
+ 
+---
+
+
+## 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.  
+
+---
+
+## 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.  
+
+---
+
+## 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
+}