STAGE 2023 Task 2 — Visual Field Sensitivity Map Prediction

400 macular OCT volumes; predict 52-point Humphrey 24-2 visual field sensitivity map (0–100 dB per point). Multi-output regression.

At a glance

Field	Value
Short name	stage_task2
Full name	STAGE 2023 Task 2 — Visual Field Sensitivity Map Prediction
Primary category	oct
Contained modalities	oct, visual_field
Tasks	regression
Samples	400
Classes	52 (VF_01, VF_02, VF_03, VF_04, VF_05, VF_06, VF_07, VF_08, VF_09, VF_10, VF_11, VF_12, VF_13, VF_14, VF_15, VF_16, VF_17, VF_18, VF_19, VF_20, VF_21, VF_22, VF_23, VF_24, VF_25, VF_26, VF_27, VF_28, VF_29, VF_30, VF_31, VF_32, VF_33, VF_34, VF_35, VF_36, VF_37, VF_38, VF_39, VF_40, VF_41, VF_42, VF_43, VF_44, VF_45, VF_46, VF_47, VF_48, VF_49, VF_50, VF_51, VF_52)
Splits	train, test
Size	5.0 GB
Source-stated terms	Non-commercial research (Baidu AI Studio)
Normalized terms	research-only
Descriptive screening label	Research or challenge restriction recorded; check source
Terms scope	unknown
Access friction	self_service_clickthrough
Route backend	Manual (upstream-gated)
Availability	available (checked 2026-07-21)
Acquisition support	guided_instructions_only
Legacy sample-loader status	Standard loader included

Notes

Labels in task2_GT_training.xlsx (52 columns per sample). All 3 tasks share the same OCT volume set.

Access preflight and acquisition

CLI

# Read-only preflight
eyehub download stage_task2 --data-dir ./data --dry-run --json

# Explicit transfer, only when preflight reports supported behavior
eyehub download stage_task2 --data-dir ./data

Python

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

ds = REGISTRY.get_dataset('stage_task2')
print(preflight_dataset(ds, './data'))  # no transfer

Upstream page: aistudio.baidu.com/aistudio

Source-term evidence: aistudio.baidu.com/aistudio

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('stage_task2')
samples = ds.load(data_dir, split='test')
for s in samples[:5]:
    print(s.sample_id, s.label, s.image_path)

Citation

BibTeX

@misc{stage_task2,
  title  = { STAGE 2023 Task 2 — Visual Field Sensitivity Map Prediction },
  note   = { MICCAI 2023 STAGE Challenge. https://aistudio.baidu.com/aistudio/competition/detail/968 },
  year   = { 2023 },
  url    = { https://aistudio.baidu.com/aistudio/competition/detail/968/0/datasets },
}

Plain text

MICCAI 2023 STAGE Challenge. https://aistudio.baidu.com/aistudio/competition/detail/968

Source-stated terms

• Raw source string: Non-commercial research (Baidu AI Studio)
• Normalized category: research-only
• Apparent scope: unknown
• 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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