Skip to main content

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

FieldValue
Short namestage_task2
Full nameSTAGE 2023 Task 2 — Visual Field Sensitivity Map Prediction
First publishedUnknown
Publication date precisionUnknown
Publication date evidenceUnknown
Publication date source fieldUnknown
Publication date reviewedUnknown
Primary categoryoct
Resource roletask_view
Dataset familystage_2023
Contained modalitiesoct, visual_field
Tasksregression
Primary reported quantity400 volumes
Classes52 (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)
Splitstrain, test
Size5.0 GB
Source-stated termsNon-commercial research (Baidu AI Studio)
Normalized termsresearch-only
Descriptive screening labelResearch or challenge restriction recorded; check source
Terms scopeunknown
Access frictionself_service_clickthrough
Route backendManual (upstream-gated)
Availabilityavailable (checked 2026-07-21)
Acquisition supportguided_instructions_only
Legacy sample-loader statusStandard loader included

Reported quantities

RoleCountUnitScopeBasisEvidence
Primary400volumesPrimary quantity reported in the reviewed catalog sourcelegacy_catalog_fieldaistudio.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

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

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_task1: STAGE 2023 Task 1 — Mean Deviation Prediction from OCT (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_task1: 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 2 and 3 use the same 400 OCT volumes and expose different labels. (evidence)

Access information and download

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

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

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

@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 },
}

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_task1: STAGE 2023 Task 1 — Mean Deviation Prediction from OCT (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)