REFUGE 2018: Retinal Fundus Glaucoma Challenge
1200 fundus images: 400 train, 400 val, 400 test. Labels: glaucoma/non-glaucoma + optic disc/cup segmentation.
At a glance
| Field | Value |
|---|---|
| Short name | refuge2018 |
| Full name | REFUGE 2018: Retinal Fundus Glaucoma Challenge |
| Primary category | fundus |
| Contained modalities | fundus |
| Tasks | classification, segmentation |
| Samples | 1,200 |
| Classes | 2 (Non-glaucoma, Glaucoma) |
| Splits | train, val, test |
| Size | 2.5 GB |
| Source-stated terms | Research only — requires Grand Challenge registration |
| Normalized terms | research-only |
| Descriptive screening label | Research or challenge restriction recorded; check source |
| Terms scope | challenge_participation |
| Access friction | controlled_or_manual |
| Route backend | Manual (upstream-gated) |
| Availability | available (checked 2026-07-21) |
| Acquisition support | manual_access_blocked |
| Legacy sample-loader status | Standard loader included |
Notes
Requires Grand Challenge account and challenge participation.
Access preflight and acquisition
- CLI
- Python
# This route requires upstream human action; no transfer starts.
eyehub download refuge2018 --data-dir ./data --dry-run --json
# Follow the official instructions shown by preflight.
from eyedatahub.acquisition import preflight_dataset
from eyedatahub.datasets.registry import REGISTRY
ds = REGISTRY.get_dataset('refuge2018')
print(preflight_dataset(ds, './data')) # returns manual_access_blocked
Upstream page: refuge.grand-challenge.org
Source-term evidence: refuge.grand-challenge.org
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('refuge2018')
samples = ds.load(data_dir, split='test')
for s in samples[:5]:
print(s.sample_id, s.label, s.image_path)
Citation
- BibTeX
- Plain text
@misc{refuge2018,
title = { REFUGE 2018: Retinal Fundus Glaucoma Challenge },
note = { Orlando et al., 'REFUGE challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs', MedIA 2020 },
year = { 2020 },
url = { https://refuge.grand-challenge.org/ },
}
Orlando et al., 'REFUGE challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs', MedIA 2020.
Source-stated terms
- Raw source string: Research only — requires Grand Challenge registration
- Normalized category:
research-only - Apparent scope:
challenge_participation - Descriptive screening label: Research or challenge restriction 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.
Related datasets with shared modalities
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cc-by-nc-nd) - eyecare_100k: Eyecare-100K: Multimodal Ophthalmology VQA Corpus (102,000 records,
unknown) - justraigs: JustRAIGS: Just Referral AI Glaucoma Screening Dataset (101,442 records,
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