| --- |
| 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.2025.3622522 |
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|
