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 |
| 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 | 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 |
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
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 fromthis 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 asrelationship 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 asrelationship with stage_task3: STAGE Tasks 2 and 3 use the same 400 OCT volumes and expose different labels. (evidence)
Access information and download
- CLI
- Python
# 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
from eyedatahub.acquisition import preflight_dataset
from eyedatahub.datasets.registry import REGISTRY
ds = REGISTRY.get_dataset('stage_task2')
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_task2')
samples = ds.load(data_dir, split='test')
for s in samples[:5]:
print(s.sample_id, s.label, s.image_path)
Citation
- BibTeX
- Plain text
@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 },
}
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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