iChallenge-AMD: Age-related Macular Degeneration

400 training fundus images with AMD classification (non-AMD vs AMD) and lesion annotations. Challenge dataset from ISBI 2020.

At a glance

Field	Value
Short name	ichallenge_amd
Full name	iChallenge-AMD: Age-related Macular Degeneration
Primary category	fundus
Contained modalities	fundus
Tasks	classification
Samples	400
Classes	2 (Non-AMD, AMD)
Splits	train, test
Size	1.5 GB
Source-stated terms	Research only — requires registration
Normalized terms	research-only
Descriptive screening label	Research or challenge restriction recorded; check source
Terms scope	dataset_files
Access friction	self_service_authenticated
Route backend	Manual (upstream-gated)
Availability	available (checked 2026-07-21)
Acquisition support	guided_instructions_only
Legacy sample-loader status	Standard loader included

Notes

Grand Challenge login is required: https://amd.grand-challenge.org/download/ The original Baidu BROAD portal has returned intermittent 5xx errors since 2024. Alternative partial resource:

• PaddleSeg optic-disc subset (~19 MB, direct HTTPS, no login): https://paddleseg.bj.bcebos.com/dataset/optic_disc_seg.zip The full 1.5 GB dataset is only via Grand Challenge or the Baidu portal.

Access preflight and acquisition

CLI

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

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

Python

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

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

Upstream page: amd.grand-challenge.org/download

Source-term evidence: amd.grand-challenge.org/download

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

Citation

BibTeX

@misc{ichallenge_amd,
  title  = { iChallenge-AMD: Age-related Macular Degeneration },
  note   = { Fu et al., 'Age-Related Macular Degeneration and Pathologic Myopia Fundus Image Analysis Challenge', ISBI 2020 },
  year   = { 2020 },
  url    = { https://amd.grand-challenge.org/download/ },
}

Plain text

Fu et al., 'Age-Related Macular Degeneration and Pathologic Myopia Fundus Image Analysis Challenge', ISBI 2020.

Source-stated terms

• Raw source string: Research only — requires registration
• 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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