Jichi Medical University Diabetic Retinopathy Dataset

9,939 color fundus images from Japanese patients with DR grading labels (Davis grading scale).

At a glance

Field	Value
Short name	jichi
Full name	Jichi Medical University Diabetic Retinopathy Dataset
Primary category	fundus
Contained modalities	fundus
Tasks	grading, classification
Samples	9,939
Classes	4 (Not reported)
Splits	all
Size	10.0 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	anonymous_direct
Route backend	Figshare
Availability	available (checked 2026-07-21)
Acquisition support	standard_platform_supported
Legacy sample-loader status	Metadata and access only

Access preflight and acquisition

CLI

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

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

Python

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

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

Upstream page: https://doi.org/10.6084/m9.figshare.4879853

Source-term evidence: https://doi.org/10.6084/m9.figshare.4879853

Loader status

This catalog record provides metadata and access instructions, but it does not yet include a standard DatasetSample loader. Inspect the source file structure or contribute a loader before using it in a training pipeline.

Citation

BibTeX

@misc{jichi,
  title  = { Jichi Medical University Diabetic Retinopathy Dataset },
  note   = { Takahashi et al., 'Applying artificial intelligence to disease staging: Deep learning for improved staging of diabetic retinopathy', PLOS ONE 2017. Article: https://pmc.ncbi.nlm.nih.gov/articles/PMC5480986/ },
  year   = { 2017 },
  url    = { https://doi.org/10.6084/m9.figshare.4879853 },
}

Plain text

Takahashi et al., 'Applying artificial intelligence to disease staging: Deep learning for improved staging of diabetic retinopathy', PLOS ONE 2017. Article: https://pmc.ncbi.nlm.nih.gov/articles/PMC5480986/

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

• airogs: AIROGS: AI for Robust Glaucoma Screening (113,893 records, 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, cc-by-nc-nd)
• eyepacs: EyePACS — Diabetic Retinopathy Detection (Kaggle 2015) (88,702 records, research-only)
• multieye: MultiEYE: OCT-Enhanced Fundus Multi-Disease Benchmark (58,036 records, mit)
• angioreport: AngioReport Fundus Angiography Report Dataset (55,361 records, unknown)
• ffa_ir: FFA-IR Medical Report Dataset (47,247 records, unknown)
• bidr: BiDR: Diabetic Retinopathy Diagnosis Dataset (35,126 records, unknown)