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README.md

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-license: mit
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+## Dataset Summary
+
+Harvard-FairDomain is a large-scale ophthalmology dataset designed for studying **fairness under domain shift** in medical image analysis. It supports both image segmentation and classification tasks, with 10,000 samples per task drawn from 10,000 unique patients. The dataset introduces an additional imaging modality — en-face fundus images — alongside the original scanning laser ophthalmoscopy (SLO) fundus images, enabling cross-domain fairness research.
+
+This dataset was introduced in the ECCV 2024 paper: [FairDomain: Achieving Fairness in Cross-Domain Medical Image Segmentation and Classification](https://arxiv.org/pdf/2407.08813).
+
+## Source Datasets
+
+Harvard-FairDomain is derived from two existing Harvard ophthalmology datasets:
+
+- [**Harvard-FairSeg**](https://github.com/Harvard-Ophthalmology-AI-Lab/FairSeg) — source for segmentation task data
+- [**FairVLMed (FairCLIP)**](https://github.com/Harvard-Ophthalmology-AI-Lab/FairCLIP) — source for classification task data
+
+En-face fundus images were added to both subsets as a new imaging domain, enabling cross-domain fairness benchmarking.
+
+---
+
+## Intended Use
+
+- **Primary use:** Research on algorithmic fairness in cross-domain medical image segmentation and classification.
+- **Out-of-scope use:** Clinical diagnosis, commercial applications, or any use prohibited by the CC BY-NC-ND 4.0 license.
+
+---
+
+## Access and Licensing
+
+- **License:** [CC BY-NC-ND 4.0](https://creativecommons.org/licenses/by-nc-nd/4.0/) — non-commercial, no derivatives, attribution required.
+- **Download:** [Google Drive link](https://drive.google.com/drive/folders/1huH93JVeXMj9rK6p1OZRub868vv0UK0O?usp=drive_link)
+- If direct download is unavailable, request access via the Google Drive link. Access is typically granted within 3–5 business days.
+- **Contact:** harvardophai@gmail.com, harvardairobotics@gmail.com
+
+> The "Harvard" designation indicates the dataset originates from the Department of Ophthalmology at Harvard Medical School. It does not imply endorsement, sponsorship, or legal responsibility by Harvard University or Harvard Medical School.
+
+---
+
+## Citation
+
+```bibtex
+@article{tian2024fairdomain,
+  title={FairDomain: Achieving Fairness in Cross-Domain Medical Image Segmentation and Classification},
+  author={Tian, Yu and Wen, Congcong and Shi, Min and Afzal, Muhammad Muneeb and Huang, Hao and Khan, Muhammad Osama and Luo, Yan and Fang, Yi and Wang, Mengyu},
+  journal={arXiv preprint arXiv:2407.08813},
+  year={2024}
+}
+```
+
+---
+
+## Authors
+
+Yu Tian, Congcong Wen, Min Shi, Muhammad Muneeb Afzal, Hao Huang, Muhammad Osama Khan, Yan Luo, Yi Fang, Mengyu Wang