STAGE 2023 Task 1 — Mean Deviation Prediction from OCT

400 macular OCT volumes; predict glaucoma Mean Deviation (MD, dB) from 24-2 Humphrey visual field test. Scalar regression task.

At a glance

Field	Value
Short name	stage_task1
Full name	STAGE 2023 Task 1 — Mean Deviation Prediction from OCT
First published	Unknown
Publication date precision	Unknown
Publication date evidence	Unknown
Publication date source field	Unknown
Publication date reviewed	Unknown
Primary category	oct
Resource role	task_view
Dataset family	stage_2023
Contained modalities	oct, visual_field
Tasks	regression
Primary reported quantity	400 volumes
Classes	Not reported (Not reported)
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

Reported quantities

Role	Count	Unit	Scope	Basis	Evidence
Primary	400	volumes	Primary quantity reported in the reviewed catalog source	legacy_catalog_field	aistudio.baidu.com/aistudio

Counts retain their source-reported units. Additional rows can describe components, paired items, or derivative copies and are not automatically added to the primary quantity.

Notes

Registration on Baidu AI Studio required. All 3 tasks share the same OCT volume set (~5 GB).

Dataset family

This record belongs to stage_2023. Family links group documented collection/component records or exact task views; they do not imply independent cohorts.

• stage_task2: STAGE 2023 Task 2 — Visual Field Sensitivity Map Prediction (task_view)
• stage_task3: STAGE 2023 Task 3 — Pattern Deviation Probability Map (task_view)

Documented relationships

These links record source-supported lineage or overlap, not merely similar modality tags.

• mm_retinal_reason is derived from this record: The version-pinned official dataset card lists this record among the CFP or OCT sources used to construct MM-Retinal-Reason. (evidence)
• This record has a documented same or overlapping cohort as relationship with stage_task2: STAGE Tasks 1 and 2 use the same 400 OCT volumes and expose different labels. (evidence)
• This record has a documented same or overlapping cohort as relationship with stage_task3: STAGE Tasks 1 and 3 use the same 400 OCT volumes and expose different labels. (evidence)

Access information and download

CLI

# Read-only preflight
eyehub download stage_task1 --data-dir ./data --dry-run --json

# Download, only when preflight reports supported behavior
eyehub download stage_task1 --data-dir ./data

Python

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

ds = REGISTRY.get_dataset('stage_task1')
print(preflight_dataset(ds, './data'))  # no download

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_task1')
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_task1,
  title  = { STAGE 2023 Task 1 — Mean Deviation Prediction from OCT },
  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.

Similar resources by shared modality

• grape: GRAPE: Glaucoma Real-world Appraisal Progression Ensemble (1,115 examinations, cc0)
• harvard_gdp: Harvard GDP: Glaucoma Detection and Progression Dataset (1,000 participants, cc-by-nc-nd)
• stage_task2: STAGE 2023 Task 2 — Visual Field Sensitivity Map Prediction (400 volumes, research-only)
• stage_task3: STAGE 2023 Task 3 — Pattern Deviation Probability Map (400 volumes, research-only)
• syn_oct: SYN-OCT Synthetic Glaucoma OCT Dataset (200,000 images, cc-by)
• multieye: MultiEYE: OCT-Enhanced Fundus Multi-Disease Benchmark (103,959 images, mit)
• eyecare_100k: Eyecare-100K: Multimodal Ophthalmology VQA Corpus (102,000 question answer pairs, unknown)
• kermany_oct: Kermany OCT 2018: Retinal OCT Image Classification (84,484 images, cc-by)