harvardairobotics/FairDomain
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--- 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*.
license: 知识共享署名-非商业性使用-禁止演绎4.0国际许可协议（CC BY-NC-ND 4.0） task_categories: - 图像分割 - 图像分类 modality: - 图像 language: - 英语 tags: - 医疗 - 眼科学 - 公平性 - 域偏移（domain shift） - 眼底 - 青光眼 pretty_name: Harvard-FairDomain size_categories: - 10000 < 样本数 < 100000 --- # 数据集卡片：Harvard-FairDomain ## 数据集概述 Harvard-FairDomain是一款大规模眼科学数据集，旨在研究医学图像分析中的**域偏移（domain shift）下的公平性**问题。该数据集支持图像分割与图像分类两类任务，每个任务均包含10000份样本，样本均来自10000名独立患者。本数据集新增了一种成像模态——正面眼底图像（en-face fundus images），并保留了原始的扫描激光眼底镜（SLO）眼底图像，可支持跨域公平性研究。 该数据集首次提出于ECCV 2024会议论文：《FairDomain：实现跨域医学图像分割与分类中的公平性》（https://arxiv.org/pdf/2407.08813）。 ## 数据集详情 ### 数据集描述 | 字段 | 取值 | |-----------------|-------| | **机构** | 哈佛医学院眼科学系 | | **任务** | 医学图像分割、医学图像分类 | | **模态** | 正面眼底图像（en-face fundus images）、扫描激光眼底镜（SLO）眼底图像 | | **样本量** | 10000份（分割任务）、10000份（分类任务） | | **患者数量** | 每个任务对应10000名独立患者 | ### 源数据 Harvard-FairDomain源自两个已有的哈佛眼科学数据集： - [**Harvard-FairSeg**](https://github.com/Harvard-Ophthalmology-AI-Lab/FairSeg) — 分割任务数据的源数据集 - [**FairVLMed（FairCLIP）**](https://github.com/Harvard-Ophthalmology-AI-Lab/FairCLIP) — 分类任务数据的源数据集 研究人员在两个子数据集的原始SLO眼底图像基础上，新增了正面眼底图像作为全新的成像域，从而支持跨域公平性基准测试。 ## 使用场景 ### 直接使用场景 针对跨域医学图像分割与分类的算法公平性开展研究，包括分析分布偏移下不同人口群体间的模型性能差异。 ### 禁止使用场景 临床诊断、商业应用，或任何违反CC BY-NC-ND 4.0许可协议的使用行为。 ## 引用格式 ### BibTeX格式： 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格式： 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预印本 arXiv:2407.08813*.
