JSIEC Fundus Photo Dataset

1,000 fundus images spanning 39 ophthalmic disease categories from the Joint Shantou International Eye Center. Used for multi-class fundus disease classification.

At a glance

Field	Value
Short name	jsiec
Full name	JSIEC Fundus Photo Dataset
Primary category	fundus
Contained modalities	fundus
Tasks	classification
Samples	1,000
Classes	39 (0.0.Normal, 0.1.Tessellated fundus, 0.2.Large optic cup, 0.3.DR1, 1.0.DR2, 1.1.DR3, 10.0.Possible glaucoma, 10.1.Optic atrophy, 11.Severe hypertensive retinopathy, 12.Disc swelling and elevation, 13.Dragged Disc, 14.Congenital disc abnormality, 15.0.Retinitis pigmentosa, 15.1.Bietti crystalline dystrophy, 16.Peripheral retinal degeneration and break, 17.Myelinated nerve fiber, 18.Vitreous particles, 19.Fundus neoplasm, 2.0.BRVO, 2.1.CRVO, 20.Massive hard exudates, 21.Yellow-white spots-flecks, 22.Cotton-wool spots, 23.Vessel tortuosity, 24.Chorioretinal atrophy-coloboma, 25.Preretinal hemorrhage, 26.Fibrosis, 27.Laser Spots, 28.Silicon oil in eye, 29.0.Blur fundus without PDR, 29.1.Blur fundus with suspected PDR, 3.RAO, 4.Rhegmatogenous RD, 5.0.CSCR, 5.1.VKH disease, 6.Maculopathy, 7.ERM, 8.MH, 9.Pathological myopia)
Splits	all
Size	0.4 GB
Source-stated terms	Other open access (Zenodo; no standard license identifier)
Normalized terms	unknown
Descriptive screening label	Unknown or unclear; do not assume permission
Terms scope	unknown
Access friction	anonymous_direct
Route backend	Zenodo
Availability	available (checked 2026-07-21)
Acquisition support	transfer_tested_partial
Legacy sample-loader status	Standard loader included

Notes

Zenodo labels the deposit as other open access but does not name a standard reuse license. Verify terms before redistribution.

Access preflight and acquisition

CLI

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

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

Python

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

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

Upstream page: zenodo.org/record

Source-term evidence: zenodo.org/record

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

Citation

BibTeX

@misc{jsiec,
  title  = { JSIEC Fundus Photo Dataset },
  note   = { Cen et al., 'Automatic detection of 39 fundus diseases and conditions in retinal photographs using deep neural networks', Nature Communications 2021. doi:10.1038/s41467-021-25138-w. Data: doi:10.5281/zenodo.3477553 },
  year   = { 2021 },
  url    = { https://zenodo.org/record/3477553 },
}

Plain text

Cen et al., 'Automatic detection of 39 fundus diseases and conditions in retinal photographs using deep neural networks', Nature Communications 2021. doi:10.1038/s41467-021-25138-w. Data: doi:10.5281/zenodo.3477553

Source-stated terms

• Raw source string: Other open access (Zenodo; no standard license identifier)
• 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.

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