Tsukazaki Hospital UWF Fundus Dataset

13,047 ultra-widefield fundus photographs (Optos, 200° FOV) from Tsukazaki Hospital, annotated with 8 binary disease labels: AO, AMD, DR, Glaucoma, MH, RD, RP, RVO. Associated with Scientific Data 2024.

At a glance

Field	Value
Short name	tsukazaki_uwf
Full name	Tsukazaki Hospital UWF Fundus Dataset
Primary category	uwf_fundus
Contained modalities	uwf_fundus
Tasks	multilabel, classification
Samples	13,047
Classes	8 (AO, AMD, DR, Gla, MH, RD, RP, RVO)
Splits	train, test
Size	15.0 GB
Source-stated terms	Research only — contact authors for data access
Normalized terms	research-only
Descriptive screening label	Research or challenge restriction recorded; check source
Terms scope	dataset_files
Access friction	author_contact
Route backend	GitHub
Availability	available (checked 2026-07-21)
Acquisition support	manual_access_blocked
Legacy sample-loader status	Standard loader included

Notes

Full dataset is subject to Japanese export restrictions. Contact the authors for access. A public subset may be available via the GitHub repo or linked Figshare.

Access preflight and acquisition

CLI

# This route requires upstream human action; no transfer starts.
eyehub download tsukazaki_uwf --data-dir ./data --dry-run --json
# Follow the official instructions shown by preflight.

Python

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

ds = REGISTRY.get_dataset('tsukazaki_uwf')
print(preflight_dataset(ds, './data'))  # returns manual_access_blocked

Upstream page: github.com/DateCazuki

Source-term evidence: github.com/DateCazuki

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

Citation

BibTeX

@misc{tsukazaki_uwf,
  title  = { Tsukazaki Hospital UWF Fundus Dataset },
  note   = { Nagasawa T et al., 'Automatic detection of multiple retinal diseases in ultra-widefield fundus images using deep learning', Nature Machine Intelligence 2022. https://www.nature.com/articles/s42256-022-00566-5 — Data: https://github.com/DateCazuki/Fundus_Diagnosis },
  year   = { 2022 },
  url    = { https://github.com/DateCazuki/Fundus_Diagnosis },
}

Plain text

Nagasawa T et al., 'Automatic detection of multiple retinal diseases in ultra-widefield fundus images using deep learning', Nature Machine Intelligence 2022. https://www.nature.com/articles/s42256-022-00566-5 — Data: https://github.com/DateCazuki/Fundus_Diagnosis

Source-stated terms

• Raw source string: Research only — contact authors for data access
• Normalized category: research-only
• Apparent scope: dataset_files
• 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.

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• oculoscope: OculoScope: Fairer AI in Ophthalmology Dataset (16,530 records, cc-by)
• birdshot_wide: Birdshot-WIDE Widefield Fundus Dataset (6,352 records, cc-by)
• deepdrid: DeepDRiD: Diabetic Retinopathy Grading and Image Quality Dataset (2,256 records, cc-by-sa)
• uwf_tumor: UWF Fundus Intraocular Tumor Dataset (2,031 records, cc-by)
• uwf_dr_peng: UWF Fundus DR Dataset (Peng et al., 2026) (1,630 records, cc-by)
• uwf_zhejiang: Open UWF Fundus Dataset with Disease + Quality Labels (700 records, cc-by)
• uwf_dr: UWF DR Reasoning Dataset (495 records, research-only)