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harvardairobotics
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Harvard-GDP

Tasks:
Image Classification
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English
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1K - 10K
Tags:
medical
ophthalmology
fairness
glaucoma
OCT
progression
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metadata 
license: cc-by-nc-nd-4.0
task_categories:
  - image-classification
modality:
  - image
language:
  - en
tags:
  - medical
  - ophthalmology
  - fairness
  - glaucoma
  - OCT
  - progression
  - semi-supervised
pretty_name: Harvard-GDP
size_categories:
  - n<1K
configs:
  - config_name: default
    data_files:
      - path: ReadMe/data_summary.csv
        split: train

Dataset Card: Harvard-GDP

Dataset Summary

Harvard-GDP (Harvard Glaucoma Detection and Progression) is a multimodal multitask ophthalmology dataset for glaucoma detection and progression forecasting. It is the largest publicly available glaucoma detection dataset with 3D OCT imaging data and the first publicly available glaucoma progression forecasting dataset. The dataset includes detailed demographic annotations (sex, race) to support fairness learning research.

This dataset was introduced at ICCV 2023: Harvard Glaucoma Detection and Progression: A Multimodal Multitask Dataset and Generalization-Reinforced Semi-Supervised Learning.

Dataset Details

Dataset Description

Field Value
Institution Department of Ophthalmology, Harvard Medical School
Tasks Glaucoma detection, glaucoma progression forecasting
Modality OCT RNFLT maps, visual field (MD, TDs)
Scale 1,000 patients, 1,000 OCT RNFLT maps
Image size 225 × 225 (RNFLT map)
License CC BY-NC-ND 4.0

Data Fields

Each subject is stored as a .npz file (data_0001.npz … data_1000.npz) in the rnflt_maps/ folder:

Field Description
rnflt OCT retinal nerve fiber layer thickness (RNFLT) map, size 225 × 225
glaucoma Glaucomatous status: 0 = non-glaucoma, 1 = glaucoma
progression Vector of 6 progression labels: 0 = non-progression, 1 = progression (first 500 subjects only)
md Mean deviation value of visual field
tds 52 total deviation values of visual field
age Patient age
male Gender: 0 = Female, 1 = Male
race Patient race
hispanic Patient ethnicity

Progression Labels

Progression labels are defined for the first 500 subjects (data_0001 – data_0500) only, using 6 criteria:

Index Criterion
progression[0] MD-based
progression[1] VFI-based
progression[2] TD pointwise
progression[3] MD fast
progression[4] MD fast (no p-value cutoff)
progression[5] TD pointwise (no p-value cutoff)

Uses

Direct Use

  • Glaucoma detection benchmarking with 3D OCT imaging data
  • Glaucoma progression forecasting (unimodal and multimodal)
  • Semi-supervised learning research with limited labeled medical data
  • Fairness and demographic disparity analysis in ophthalmic 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:

@inproceedings{luo2023harvard,
  title={Harvard Glaucoma Detection and Progression: A Multimodal Multitask Dataset and Generalization-Reinforced Semi-Supervised Learning},
  author={Luo, Yan and Shi, Min and Tian, Yu and Elze, Tobias and Wang, Mengyu},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={20471--20482},
  year={2023}
}

APA:

Luo, Y., Shi, M., Tian, Y., Elze, T., & Wang, M. (2023). Harvard Glaucoma Detection and Progression: A Multimodal Multitask Dataset and Generalization-Reinforced Semi-Supervised Learning. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV 2023), 20471–20482.