| --- |
| license: cc-by-nc-nd-4.0 |
| task_categories: |
| - image-segmentation |
| - image-classification |
| modality: |
| - image |
| language: |
| - en |
| tags: |
| - medical |
| - ophthalmology |
| - fairness |
| - domain-shift |
| - fundus |
| - glaucoma |
| pretty_name: Harvard-FairDomain |
| size_categories: |
| - 10K<n<100K |
| --- |
| |
| # Dataset Card: Harvard-FairDomain |
|
|
| ## 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). |
|
|
| ## Dataset Details |
|
|
| ### Dataset Description |
|
|
| | Field | Value | |
| |-----------------|-------| |
| | **Institution** | Department of Ophthalmology, Harvard Medical School | |
| | **Tasks** | Medical image segmentation, medical image classification | |
| | **Modalities** | En-face fundus image, scanning laser ophthalmoscopy (SLO) fundus image | |
| | **Samples** | 10,000 (segmentation), 10,000 (classification) | |
| | **Patients** | 10,000 per task (unique patients) | |
|
|
| ### Source Data |
|
|
| 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 on top of the original SLO fundus images, enabling cross-domain fairness benchmarking. |
|
|
| ## Uses |
|
|
| ### Direct Use |
|
|
| Research on algorithmic fairness in cross-domain medical image segmentation and classification, including studies of model performance disparities across demographic groups under distribution shift. |
|
|
| ### Out-of-Scope Use |
|
|
| Clinical diagnosis, commercial applications, or any use prohibited by the CC BY-NC-ND 4.0 license. |
|
|
| ## Citation |
|
|
| **BibTeX:** |
|
|
| ```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} |
| } |
| ``` |
|
|
| **APA:** |
|
|
| Tian, Y., Wen, C., Shi, M., Afzal, M. M., Huang, H., Khan, M. O., Luo, Y., Fang, Y., & Wang, M. (2024). FairDomain: Achieving Fairness in Cross-Domain Medical Image Segmentation and Classification. *arXiv preprint arXiv:2407.08813*. |
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|
