STAGE 2023 Task 3 — Pattern Deviation Probability Map

400 macular OCT volumes; predict 52-point pattern deviation probability map from 24-2 Humphrey visual field test. Multi-output regression.

At a glance

Field	Value
Short name	stage_task3
Full name	STAGE 2023 Task 3 — Pattern Deviation Probability Map
Primary category	oct
Contained modalities	oct, visual_field
Tasks	regression
Samples	400
Classes	52 (PD_01, PD_02, PD_03, PD_04, PD_05, PD_06, PD_07, PD_08, PD_09, PD_10, PD_11, PD_12, PD_13, PD_14, PD_15, PD_16, PD_17, PD_18, PD_19, PD_20, PD_21, PD_22, PD_23, PD_24, PD_25, PD_26, PD_27, PD_28, PD_29, PD_30, PD_31, PD_32, PD_33, PD_34, PD_35, PD_36, PD_37, PD_38, PD_39, PD_40, PD_41, PD_42, PD_43, PD_44, PD_45, PD_46, PD_47, PD_48, PD_49, PD_50, PD_51, PD_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

Access preflight and acquisition

CLI

# Read-only preflight
eyehub download stage_task3 --data-dir ./data --dry-run --json

# Explicit transfer, only when preflight reports supported behavior
eyehub download stage_task3 --data-dir ./data

Python

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

ds = REGISTRY.get_dataset('stage_task3')
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_task3')
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_task3,
  title  = { STAGE 2023 Task 3 — Pattern Deviation Probability Map },
  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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• harvard_gdp: Harvard GDP: Glaucoma Detection and Progression Dataset (1,000 records, cc-by-nc-nd)
• stage_task1: STAGE 2023 Task 1 — Mean Deviation Prediction from OCT (400 records, research-only)
• stage_task2: STAGE 2023 Task 2 — Visual Field Sensitivity Map Prediction (400 records, research-only)
• syn_oct: SYN-OCT Synthetic Glaucoma OCT Dataset (200,000 records, cc-by)
• eyecare_100k: Eyecare-100K: Multimodal Ophthalmology VQA Corpus (102,000 records, unknown)
• kermany_oct: Kermany OCT 2018: Retinal OCT Image Classification (84,484 records, cc-by)
• multieye: MultiEYE: OCT-Enhanced Fundus Multi-Disease Benchmark (58,036 records, mit)