Dataset Card: Harvard-GD

Dataset Summary

Harvard-GD (Harvard Glaucoma Detection) is an ophthalmology dataset of OCT retinal nerve fiber layer thickness (RNFLT) maps from 500 unique glaucoma patients. It includes glaucoma labels and visual field mean deviation (MD) values, and was released alongside the EyeLearn framework for artifact-tolerant contrastive representation learning on ophthalmic images.

This dataset was introduced in the IEEE Journal of Biomedical and Health Informatics 2023: Artifact-Tolerant Clustering-Guided Contrastive Embedding Learning for Ophthalmic Images in Glaucoma.

Dataset Details

Dataset Description

Field	Value
Institution	Department of Ophthalmology, Harvard Medical School
Task	Glaucoma detection
Modality	OCT RNFLT maps
Scale	500 patients, 500 OCT RNFLT maps
Image size	225 × 225 (RNFLT map)
License	CC BY-NC-ND 4.0

• Curated by: Min Shi, Anagha Lokhande, Mojtaba S. Fazli, Vishal Sharma, Yu Tian, Yan Luo, Louis R. Pasquale, Tobias Elze, Michael V. Boland, Nazlee Zebardast, David S. Friedman, Lucy Q. Shen, Mengyu Wang
• License: CC BY-NC-ND 4.0 — non-commercial research only
• Paper: IEEE JBHI 2023
• Contact: harvardophai@gmail.com, harvardairobotics@gmail.com

Data Fields

The dataset is stored as a .npy file (rnflt_map.npy) containing RNFLT maps for all 500 subjects:

Field	Description
rnflt	OCT retinal nerve fiber layer thickness (RNFLT) map, size 225 × 225
glaucoma	Glaucomatous status: 0 = non-glaucoma, 1 = glaucoma
md	Mean deviation value of visual field

Uses

Direct Use

• Glaucoma detection benchmarking with OCT RNFLT imaging
• Representation learning and contrastive embedding research for ophthalmic images
• Artifact detection and correction in RNFLT maps
• Pretraining and evaluation of ophthalmic image encoders

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:

@article{10159482,
  author={Shi, Min and Lokhande, Anagha and Fazli, Mojtaba S. and Sharma, Vishal and Tian, Yu and Luo, Yan and Pasquale, Louis R. and Elze, Tobias and Boland, Michael V. and Zebardast, Nazlee and Friedman, David S. and Shen, Lucy Q. and Wang, Mengyu},
  journal={IEEE Journal of Biomedical and Health Informatics},
  title={Artifact-Tolerant Clustering-Guided Contrastive Embedding Learning for Ophthalmic Images in Glaucoma},
  year={2023},
  volume={27},
  number={9},
  pages={4329-4340},
  doi={10.1109/JBHI.2023.3288830}
}

APA:

Shi, M., Lokhande, A., Fazli, M. S., Sharma, V., Tian, Y., Luo, Y., Pasquale, L. R., Elze, T., Boland, M. V., Zebardast, N., Friedman, D. S., Shen, L. Q., & Wang, M. (2023). Artifact-Tolerant Clustering-Guided Contrastive Embedding Learning for Ophthalmic Images in Glaucoma. IEEE Journal of Biomedical and Health Informatics, 27(9), 4329–4340. https://doi.org/10.1109/JBHI.2023.3288830