JustRAIGS: Just Referral AI Glaucoma Screening Dataset

101,442 gradable fundus images labeled as referable (RG) or non-referable (NRG) for glaucoma. Image data from EyePACS LLC; labels from Rotterdam Eye Hospital expert graders.

At a glance

Field	Value
Short name	justraigs
Full name	JustRAIGS: Just Referral AI Glaucoma Screening Dataset
Primary category	fundus
Contained modalities	fundus
Tasks	classification
Samples	101,442
Classes	2 (No Referable Glaucoma, Referable Glaucoma)
Splits	train
Size	100.0 GB
Source-stated terms	CC BY-NC-ND 4.0
Normalized terms	cc-by-nc-nd
Descriptive screening label	Explicit noncommercial clause recorded; check source
Terms scope	dataset_files
Access friction	self_service_clickthrough
Route backend	Zenodo
Availability	available (checked 2026-07-21)
Acquisition support	standard_platform_supported
Legacy sample-loader status	Standard loader included

Notes

Large dataset (~100 GB). The record is publicly accessible but downloading requires accepting the CC BY-NC-ND terms on the Zenodo page. Set ZENODO_TOKEN in .env if access is denied.

Access preflight and acquisition

CLI

# Read-only preflight
eyehub download justraigs --data-dir ./data --dry-run --json

# Explicit transfer, only when preflight reports supported behavior
eyehub download justraigs --data-dir ./data

Python

from eyedatahub.acquisition import preflight_dataset
from eyedatahub.datasets.registry import REGISTRY

ds = REGISTRY.get_dataset('justraigs')
print(preflight_dataset(ds, './data'))  # no transfer

Upstream page: zenodo.org/records

Source-term evidence: zenodo.org/records

Loader example

This entry includes a standard DatasetSample loader.

from pathlib import Path
from eyedatahub.datasets.registry import REGISTRY

data_dir = Path('~/.eyedatahub/data').expanduser()
ds = REGISTRY.get_dataset('justraigs')
samples = ds.load(data_dir, split='train')
for s in samples[:5]:
    print(s.sample_id, s.label, s.image_path)

Citation

BibTeX

@misc{justraigs,
  title  = { JustRAIGS: Just Referral AI Glaucoma Screening Dataset },
  note   = { Thakoor et al., 'JustRAIGS: Justify Your Artificial Intelligence Prediction for Referable or Not Referable Glaucoma Screening', MICCAI 2023. Zenodo: https://zenodo.org/records/10035093 },
  year   = { 2023 },
  url    = { https://zenodo.org/records/10035093 },
}

Plain text

Thakoor et al., 'JustRAIGS: Justify Your Artificial Intelligence Prediction for Referable or Not Referable Glaucoma Screening', MICCAI 2023. Zenodo: https://zenodo.org/records/10035093

Source-stated terms

• Raw source string: CC BY-NC-ND 4.0
• Normalized category: cc-by-nc-nd
• Apparent scope: dataset_files
• Descriptive screening label: Explicit noncommercial clause recorded; check source

⚠️ Source-stated terms, scope, and normalized labels are curation metadata, not legal advice or a permission finding. Review the current official source before transfer or reuse.

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• eyepacs: EyePACS — Diabetic Retinopathy Detection (Kaggle 2015) (88,702 records, research-only)
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• angioreport: AngioReport Fundus Angiography Report Dataset (55,361 records, unknown)
• ffa_ir: FFA-IR Medical Report Dataset (47,247 records, unknown)
• bidr: BiDR: Diabetic Retinopathy Diagnosis Dataset (35,126 records, unknown)
• dr_arranged: Diabetic Retinopathy Arranged Dataset (Tianchi 93926) (35,126 records, cc-by-nc-sa)