---
license: cc-by-nc-nd-4.0
task_categories:
  - image-classification
  - text-to-image
modality:
  - image
language:
  - en
tags:
  - medical
  - ophthalmology
  - fairness
  - generative
  - diffusion
  - fundus
  - glaucoma
  - OCT
pretty_name: FairGenMed
size_categories:
  - 10K<n<100K
configs:
  - config_name: default
    data_files:
      - path: ReadMe/data_summary.csv
        split: train
---

# Dataset Card: FairGenMed

## Dataset Summary

FairGenMed is the first dataset for studying **fairness in medical generative models**. It provides detailed quantitative clinical measurements alongside demographic annotations to investigate the semantic correlation between text prompts and anatomical regions across demographic subgroups. The dataset supports both generative model evaluation and downstream classification tasks for glaucoma detection.

This dataset accompanies the **FairDiffusion** framework — an equity-aware latent diffusion model that enhances fairness in medical image generation via Fair Bayesian Perturbation — published in *Science Advances* (2025).

## Dataset Details

### Dataset Description

| Field            | Value |
|------------------|-------|
| **Institution**  | Department of Ophthalmology, Harvard Medical School |
| **Task**         | Glaucoma detection; fairness evaluation of generative models |
| **Modality**     | Scanning Laser Ophthalmoscopy (SLO) fundus images, OCT B-scans |
| **Scale**        | 10,000 subjects |
| **Image size**   | 512 × 664 (SLO fundus) |
| **License**      | [CC BY-NC-ND 4.0](https://creativecommons.org/licenses/by-nc-nd/4.0/) |

- **Curated by:** Yan Luo, Muhammad Osama Khan, Congcong Wen, Muhammad Muneeb Afzal, Titus Fidelis Wuermeling, Min Shi, Yu Tian, Yi Fang, Mengyu Wang
- **License:** [CC BY-NC-ND 4.0](https://creativecommons.org/licenses/by-nc-nd/4.0/) — non-commercial research only
- **Paper:** [Science Advances, Vol. 11, No. 14 (2025)](https://doi.org/10.1126/sciadv.ads4593)
- **Contact:** harvardophai@gmail.com, harvardairobotics@gmail.com

### Data Fields

Each subject includes one SLO fundus image and one `.npz` file. The NPZ files contain:

| Field           | Description |
|-----------------|-------------|
| `glaucoma`      | Disease label: `0` = non-glaucoma, `1` = glaucoma |
| `oct_bscans`    | OCT B-scan images |
| `race`          | `0` = Asian, `1` = Black, `2` = White |
| `male`          | `0` = Female, `1` = Male |
| `hispanic`      | `0` = Non-Hispanic, `1` = Hispanic |
| `maritalstatus` | `0` = Married/Partnered, `1` = Single, `2` = Divorced, `3` = Widowed, `4` = Legally Separated, `-1` = Unknown |
| `language`      | `0` = English, `1` = Spanish, `2` = Other |

### Clinical Metadata

All clinical measurements for the 10,000 samples are provided in `data_summary.csv`:

| Column        | Description |
|---------------|-------------|
| `cdr_status`  | Cup-disc ratio status |
| `md_severity` | Severity of vision loss |
| `se_status`   | Spherical equivalent status |

### Demographics

6 demographic attributes are annotated per subject: age, gender, race, ethnicity, preferred language, and marital status.

## Uses

### Direct Use

- Fairness evaluation of medical generative models (text-to-image diffusion)
- Glaucoma detection with demographic fairness analysis
- Studying semantic correlations between text prompts and anatomy across subgroups

### Out-of-Scope Use

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

## Associated Method: FairDiffusion

FairDiffusion is an equity-aware latent diffusion model built on Stable Diffusion 2.1, trained with **Fair Bayesian Perturbation** to reduce demographic bias in generated medical images. It is evaluated on FairGenMed (ophthalmology), HAM10000 (dermatology), and CheXpert (chest radiology).

## Citation

**BibTeX:**

```bibtex
@article{FairDiffusion_Science_Advances_2025,
  author = {Yan Luo and Muhammad Osama Khan and Congcong Wen and Muhammad Muneeb Afzal and Titus Fidelis Wuermeling and Min Shi and Yu Tian and Yi Fang and Mengyu Wang},
  title = {FairDiffusion: Enhancing equity in latent diffusion models via fair Bayesian perturbation},
  journal = {Science Advances},
  volume = {11},
  number = {14},
  pages = {eads4593},
  year = {2025},
  doi = {10.1126/sciadv.ads4593}
}
```

**APA:**

Luo, Y., Khan, M. O., Wen, C., Afzal, M. M., Wuermeling, T. F., Shi, M., Tian, Y., Fang, Y., & Wang, M. (2025). FairDiffusion: Enhancing equity in latent diffusion models via fair Bayesian perturbation. *Science Advances, 11*(14), eads4593. https://doi.org/10.1126/sciadv.ads4593