Datasets:

harvardairobotics
/

FairDomain

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.

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 — source for segmentation task data
FairVLMed (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:

@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.