Kermany OCT 2018: Retinal OCT Image Classification

~84,000 retinal OCT B-scan images across 4 classes: CNV, DME, DRUSEN, NORMAL. Train: ~83,484 / Test: 1000.

At a glance

Field	Value
Short name	kermany_oct
Full name	Kermany OCT 2018: Retinal OCT Image Classification
Primary category	oct
Contained modalities	oct
Tasks	classification
Samples	84,484
Classes	4 (CNV, DME, DRUSEN, NORMAL)
Splits	train, test, val
Size	6.0 GB
Source-stated terms	CC BY 4.0
Normalized terms	cc-by
Descriptive screening label	Standard label without an explicit NC clause; not a permission finding
Terms scope	dataset_files
Access friction	self_service_authenticated
Route backend	Kaggle
Availability	available (checked 2026-07-21)
Acquisition support	standard_platform_supported
Legacy sample-loader status	Standard loader included

Access preflight and acquisition

CLI

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

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

Python

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

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

Upstream page: kaggle.com/datasets

Source-term evidence: kaggle.com/datasets

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('kermany_oct')
samples = ds.load(data_dir, split='test')
for s in samples[:5]:
    print(s.sample_id, s.label, s.image_path)

Citation

BibTeX

@misc{kermany_oct,
  title  = { Kermany OCT 2018: Retinal OCT Image Classification },
  note   = { Kermany et al., 'Identifying medical diagnoses and treatable diseases by image-based deep learning', Cell 2018 },
  year   = { 2018 },
  url    = { https://www.kaggle.com/datasets/paultimothymooney/kermany2018 },
}

Plain text

Kermany et al., 'Identifying medical diagnoses and treatable diseases by image-based deep learning', Cell 2018.

Source-stated terms

• Raw source string: CC BY 4.0
• Normalized category: cc-by
• Apparent scope: dataset_files
• Descriptive screening label: Standard label without an explicit NC clause; not a permission finding

⚠️ 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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• multieye: MultiEYE: OCT-Enhanced Fundus Multi-Disease Benchmark (58,036 records, mit)
• lmod_plus: LMOD+ Multimodal Ophthalmology Benchmark (32,633 records, unknown)
• harvard_fairvision: Harvard-FairVision (AMD + DR + Glaucoma, paired SLO + OCT) (30,000 records, cc-by-nc-nd)
• mario: MARIO: AMD-Progression Longitudinal OCT (MICCAI 2024) (30,000 records, cc-by)
• mmrdr: MMRDR: Multi-Modal Retinal Diabetic Retinopathy Dataset (24,460 records, cc-by)
• oct_c8: Retinal OCT-C8: 8-Class OCT Classification (24,000 records, unknown)