Datasets:

harvardairobotics
/

FairVision

license: cc-by-nc-nd-4.0
task_categories:
  - image-classification
modality:
  - image
language:
  - en
tags:
  - medical
  - ophthalmology
  - fairness
  - glaucoma
  - AMD
  - diabetic-retinopathy
  - OCT
  - fundus
pretty_name: Harvard-FairVision
size_categories:
  - 10K<n<100K
configs:
  - config_name: glaucoma
    data_files:
      - path: Glaucoma/ReadMe/data_summary_glaucoma.csv
        split: train
  - config_name: dr
    data_files:
      - path: DR/ReadMe/data_summary_dr.csv
        split: train
  - config_name: amd
    data_files:
      - path: AMD/ReadMe/data_summary_amd.csv
        split: train

Dataset Card: Harvard-FairVision

Dataset Summary

Harvard-FairVision is the first large-scale medical fairness dataset with both 2D and 3D imaging data, covering three major eye diseases affecting approximately 380 million people worldwide. It contains 30,000 subjects (10,000 per disease) across Age-Related Macular Degeneration (AMD), Diabetic Retinopathy (DR), and glaucoma, each with paired SLO fundus photos and 3D OCT B-scans and six demographic identity attributes.

This dataset was introduced in the paper: FairVision: Equitable Deep Learning for Eye Disease Screening via Fair Identity Scaling.

Dataset Details

Dataset Description

Field	Value
Institution	Department of Ophthalmology, Harvard Medical School
Tasks	AMD detection, diabetic retinopathy detection, glaucoma detection
Modalities	Scanning Laser Ophthalmoscopy (SLO) fundus images, 3D OCT B-scans
Scale	30,000 subjects (10,000 per disease)
OCT size	200 × 200 × 200 (glaucoma), 128 × 200 × 200 (AMD, DR)
SLO size	512 × 664 (folders), 200 × 200 (NPZ, normalized to [0, 255])
Total size	~600 GB
License	CC BY-NC-ND 4.0

Curated by: Yan Luo, Muhammad Osama Khan, Yu Tian, Min Shi, Zehao Dou, Tobias Elze, Yi Fang, Mengyu Wang
License: CC BY-NC-ND 4.0 — non-commercial research only
Paper: arXiv:2310.02492
Contact: harvardophai@gmail.com, harvardairobotics@gmail.com

Dataset Structure

FairVision
├── AMD
│   ├── Training
│   ├── Validation
│   └── Test
├── data_summary_amd.csv
├── DR
│   ├── Training
│   ├── Validation
│   └── Test
├── data_summary_dr.csv
├── Glaucoma
│   ├── Training
│   ├── Validation
│   └── Test
└── data_summary_glaucoma.csv

Each split folder contains SLO fundus photos (slo_xxxxx.jpg) and NPZ files (data_xxxxx.npz). Per-disease metadata CSVs (data_summary_*.csv) provide race, gender, ethnicity, marital status, age, and preferred language for all subjects.

Data Fields

All NPZ files share the following demographic and imaging fields:

Field	Description
`slo_fundus`	SLO fundus image, 200 × 200 (normalized)
`oct_bscans`	3D OCT B-scans (200 × 200 × 200 for glaucoma; 128 × 200 × 200 for AMD/DR)
`race`	`0` = Asian, `1` = Black, `2` = White
`male`	`0` = Female, `1` = Male
`hispanic`	`0` = Non-Hispanic, `1` = Hispanic
`maritalstatus`	`0` = Married, `1` = Single, `2` = Divorced, `3` = Widowed, `4` = Legally Separated
`language`	`0` = English, `1` = Spanish, `2` = Other

Disease-specific label fields:

Disease	Field	Values
Glaucoma	`glaucoma`	`0` = non-glaucoma, `1` = glaucoma
AMD	`amd_condition`	9-class condition string, mapped to `0` = no AMD, `1` = early dry, `2` = intermediate dry, `3` = advanced
DR	`dr_subtype`	6-class condition string, mapped to `0` = non-vision-threatening, `1` = vision-threatening (severe NPDR or PDR)

Uses

Direct Use

Fairness benchmarking for 2D and 3D ophthalmic disease classification across race, gender, and ethnicity
Multi-disease fairness analysis (AMD, DR, glaucoma) under a unified framework
Development and evaluation of fairness learning methods for medical imaging
Comparative study of 2D vs. 3D model fairness in clinical AI

Out-of-Scope Use

Clinical decisions, patient care, or any commercial application. This dataset shall not be used for clinical decisions at any time.

Access

The "Harvard" designation indicates the dataset originates from the Department of Ophthalmology at Harvard Medical School. It does not imply endorsement, sponsorship, or legal responsibility by Harvard University or Harvard Medical School.

Citation

BibTeX:

@misc{luo2024fairvisionequitabledeeplearning,
  title={FairVision: Equitable Deep Learning for Eye Disease Screening via Fair Identity Scaling},
  author={Yan Luo and Muhammad Osama Khan and Yu Tian and Min Shi and Zehao Dou and Tobias Elze and Yi Fang and Mengyu Wang},
  year={2024},
  eprint={2310.02492},
  archivePrefix={arXiv},
  primaryClass={cs.CV},
  url={https://arxiv.org/abs/2310.02492}
}

APA:

Luo, Y., Khan, M. O., Tian, Y., Shi, M., Dou, Z., Elze, T., Fang, Y., & Wang, M. (2024). FairVision: Equitable Deep Learning for Eye Disease Screening via Fair Identity Scaling. arXiv preprint arXiv:2310.02492.