Retinal OCT-C8: 8-Class OCT Classification

~24,000 retinal OCT images across 8 disease classes: AMD, BRAO, BRVO, CSC, CRAO, CRVO, DME, MH.

At a glance

Field	Value
Short name	oct_c8
Full name	Retinal OCT-C8: 8-Class OCT Classification
Primary category	oct
Contained modalities	oct
Tasks	classification
Samples	24,000
Classes	8 (AMD, Branch Retinal Artery Occlusion, Branch Retinal Vein Occlusion, Central Serous Chorioretinopathy, Central Retinal Artery Occlusion, Central Retinal Vein Occlusion, Diabetic Macular Edema, Macular Hole)
Splits	train, val, test
Size	2.5 GB
Source-stated terms	See Kaggle dataset page
Normalized terms	unknown
Descriptive screening label	Unknown or unclear; do not assume permission
Terms scope	unknown
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 oct_c8 --data-dir ./data --dry-run --json

# Explicit transfer, only when preflight reports supported behavior
eyehub download oct_c8 --data-dir ./data

Python

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

ds = REGISTRY.get_dataset('oct_c8')
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('oct_c8')
samples = ds.load(data_dir, split='test')
for s in samples[:5]:
    print(s.sample_id, s.label, s.image_path)

Citation

BibTeX

@misc{oct_c8,
  title  = { Retinal OCT-C8: 8-Class OCT Classification },
  note   = { Srinivasan et al., 'Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images', Biomed. Opt. Express 2014 },
  year   = { 2014 },
  url    = { https://www.kaggle.com/datasets/obulisainaren/retinal-oct-c8 },
}

Plain text

Srinivasan et al., 'Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images', Biomed. Opt. Express 2014.

Source-stated terms

• Raw source string: See Kaggle dataset page
• Normalized category: unknown
• Apparent scope: unknown
• Descriptive screening label: Unknown or unclear; do not assume permission

⚠️ 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

• syn_oct: SYN-OCT Synthetic Glaucoma OCT Dataset (200,000 records, cc-by)
• eyecare_100k: Eyecare-100K: Multimodal Ophthalmology VQA Corpus (102,000 records, unknown)
• kermany_oct: Kermany OCT 2018: Retinal OCT Image Classification (84,484 records, cc-by)
• 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)