harvardairobotics/FairFedMed

--- license: mit task_categories: - image-classification modality: - image language: - en tags: - medical - ophthalmology - radiology - fairness - federated-learning - fundus - glaucoma - chest-xray - OCT pretty_name: FairFedMed size_categories: - 10K<n<100K --- # Dataset Card: FairFedMed ## Dataset Summary FairFedMed is the first federated learning (FL) benchmark dataset for medical imaging with demographic annotations, designed to study **group fairness across institutions** in a federated setting. It comprises two subsets spanning ophthalmology and chest radiology, enabling research on fairness-aware federated learning under realistic cross-institutional data heterogeneity. This dataset was introduced in the IEEE Transactions on Medical Imaging 2025 paper: [FairFedMed: Benchmarking Group Fairness in Federated Medical Imaging with FairLoRA](https://ieeexplore.ieee.org/document/11205878). ## Dataset Details ### Dataset Description - **Curated by:** Minghan Li, Congcong Wen, Yu Tian, Min Shi, Yan Luo, Hao Huang, Yi Fang, Mengyu Wang - **Institution:** Harvard Medical School / Harvard AI and Robotics Lab - **License:** See individual subset licenses (CheXpert and MIMIC-CXR have their own terms) - **Repository:** [Harvard-AI-and-Robotics-Lab/FairFedMed](https://github.com/Harvard-AI-and-Robotics-Lab/FairFedMed) - **Paper:** [IEEE TMI 2025](https://ieeexplore.ieee.org/document/11205878) / [arXiv:2508.00873](https://arxiv.org/abs/2508.00873) ### Subsets #### FairFedMed-Oph (Ophthalmology) | Field | Value | |------------------|-------| | **Task** | Glaucoma detection (binary classification) | | **Modalities** | 2D SLO fundus images, 3D OCT B-scans | | **Scale** | 15,165 patients | | **Demographics** | Age, gender, race, ethnicity, preferred language, marital status (6 attributes) | | **FL Setup** | Multi-site federated (3 sites) | #### FairFedMed-Chest (Chest Radiology) | Field | Value | |------------------|-------| | **Task** | Chest pathology classification | | **Sources** | [CheXpert](https://stanfordmlgroup.github.io/competitions/chexpert/) + [MIMIC-CXR](https://physionet.org/content/mimic-cxr/2.1.0/) | | **Demographics** | Age, gender, race (3 attributes) | | **FL Setup** | 2 clients simulating cross-institutional FL | ## Uses ### Direct Use Research on group fairness in federated medical image classification, including studies of demographic disparity across institutions and evaluation of fairness-aware FL methods. ### Out-of-Scope Use Clinical diagnosis, commercial applications. Note that FairFedMed-Chest inherits the usage restrictions of CheXpert and MIMIC-CXR — consult those datasets' licenses before use. ## Evaluation | Metric | Description | |-------------|-------------------------------------| | **AUC** | Area Under ROC Curve | | **ESAUC** | Equalized Selection AUC | | **EOD** | Equalized Odds Difference | | **SPD** | Statistical Parity Difference | | **Group AUC** | Per-demographic-group AUC | ## Associated Method: FairLoRA The paper introduces **FairLoRA**, a fairness-aware FL framework using SVD-based low-rank adaptation. It customizes singular values per demographic group while sharing singular vectors across clients for communication efficiency. Supported backbones: ViT-B/16, ResNet-50. ## Citation **BibTeX:** ```bibtex @ARTICLE{11205878, author={Li, Minghan and Wen, Congcong and Tian, Yu and Shi, Min and Luo, Yan and Huang, Hao and Fang, Yi and Wang, Mengyu}, journal={IEEE Transactions on Medical Imaging}, title={FairFedMed: Benchmarking Group Fairness in Federated Medical Imaging with FairLoRA}, year={2025}, pages={1-1}, doi={10.1109/TMI.2025.3622522} } ``` **APA:** Li, M., Wen, C., Tian, Y., Shi, M., Luo, Y., Huang, H., Fang, Y., & Wang, M. (2025). FairFedMed: Benchmarking Group Fairness in Federated Medical Imaging with FairLoRA. *IEEE Transactions on Medical Imaging*. https://doi.org/10.1109/TMI.2025.3622522license: MIT task_categories: - 图像分类 modality: - 图像 language: - en tags: - 医学 - 眼科学 - 放射学 - 公平性 - 联邦学习（Federated Learning, FL） - 眼底（fundus） - 青光眼 - 胸部X光（chest-xray） - 光学相干断层扫描（Optical Coherence Tomography, OCT） pretty_name: FairFedMed size_categories: - 10K<n<100K # 数据集卡片：FairFedMed ## 数据集概述 FairFedMed是首个带有人口统计学标注的医学影像联邦学习基准数据集，旨在研究联邦场景下跨机构的群体公平性问题。该数据集包含眼科与胸部放射学两个子数据集，可支持在真实的跨机构数据异质性条件下开展公平感知型联邦学习研究。 该数据集发表于《IEEE医学成像汇刊》2025年的论文：[FairFedMed：基于FairLoRA的联邦医学影像群体公平性基准测试](https://ieeexplore.ieee.org/document/11205878)。 ## 数据集详情 ### 数据集描述 - **整理者：** 李明翰、温聪聪、田宇、史敏、罗艳、黄浩、方毅、王梦宇 - **所属机构：** 哈佛医学院 / 哈佛人工智能与机器人实验室 - **许可证：** 请参阅各子数据集许可证（CheXpert与MIMIC-CXR拥有各自的使用条款） - **代码仓库：** [Harvard-AI-and-Robotics-Lab/FairFedMed](https://github.com/Harvard-AI-and-Robotics-Lab/FairFedMed) - **相关论文：** [IEEE TMI 2025](https://ieeexplore.ieee.org/document/11205878) / [arXiv:2508.00873](https://arxiv.org/abs/2508.00873) ### 子数据集 #### FairFedMed-Oph（眼科子数据集） | 字段 | 数值 | |---------------------|-------| | **任务** | 青光眼检测（二分类任务） | | **模态** | 二维扫描激光眼底镜（Scanning Laser Ophthalmoscope, SLO）眼底图像、三维光学相干断层扫描（Optical Coherence Tomography, OCT）B扫描图像 | | **规模** | 15165名患者 | | **人口统计学属性** | 年龄、性别、种族、族群、偏好语言、婚姻状况（共6项属性） | | **联邦学习设置** | 多站点联邦学习（3个站点） | #### FairFedMed-Chest（胸部放射学子数据集） | 字段 | 数值 | |---------------------|-------| | **任务** | 胸部病理分类 | | **数据来源** | [CheXpert](https://stanfordmlgroup.github.io/competitions/chexpert/) + [MIMIC-CXR](https://physionet.org/content/mimic-cxr/2.1.0/) | | **人口统计学属性** | 年龄、性别、种族（共3项属性） | | **联邦学习设置** | 2个客户端模拟跨机构联邦学习 | ## 使用场景 ### 直接使用场景 开展联邦医学影像分类中的群体公平性研究，包括跨机构人口统计学差异分析以及公平感知型联邦学习方法的评估。 ### 禁止使用场景 临床诊断、商业应用。请注意FairFedMed-Chest继承了CheXpert与MIMIC-CXR的使用限制，使用前请查阅这两个数据集的许可证条款。 ## 评估指标 | 评估指标 | 说明 | |---------------|------------------------------| | **AUC** | 受试者工作特征曲线下面积（Area Under ROC Curve, AUC） | | **ESAUC** | 均衡选择AUC（Equalized Selection AUC） | | **EOD** | 均衡赔率差（Equalized Odds Difference） | | **SPD** | 统计奇偶差（Statistical Parity Difference） | | **Group AUC** | 各人口统计学分组的AUC值 | ## 关联方法：FairLoRA 该论文提出了**FairLoRA**，一种基于奇异值分解（Singular Value Decomposition, SVD）的低秩适配的公平感知型联邦学习框架。该方法针对每个人口统计学分组自定义奇异值，同时在客户端之间共享奇异向量以提升通信效率。 支持的骨干网络：ViT-B/16、ResNet-50。 ## 引用 ### BibTeX格式引用 bibtex @ARTICLE{11205878, author={Li, Minghan and Wen, Congcong and Tian, Yu and Shi, Min and Luo, Yan and Huang, Hao and Fang, Yi and Wang, Mengyu}, journal={IEEE Transactions on Medical Imaging}, title={FairFedMed: Benchmarking Group Fairness in Federated Medical Imaging with FairLoRA}, year={2025}, pages={1-1}, doi={10.1109/TMI.2025.3622522} } ### APA格式引用 Li, M., Wen, C., Tian, Y., Shi, M., Luo, Y., Huang, H., Fang, Y., & Wang, M. (2025). FairFedMed: Benchmarking Group Fairness in Federated Medical Imaging with FairLoRA. *IEEE Transactions on Medical Imaging*. https://doi.org/10.1109/TMI.2025.3622522