Development of a deep learning model for epiretinal membrane detection in fundus photography
Fundus-image-level erm observations/labels from Retrospectively collected colour-fundus photographs from patients diagnosed with epiretinal membrane at B&VIIT Eye Center, Seoul.
At a glance
| Field | Value |
|---|---|
| Short name | mendeley_development_deep_learning_model_epiretinal_membrane |
| Full name | Development of a deep learning model for epiretinal membrane detection in fundus photography |
| First published | 2023-07-31 |
| Publication date precision | day |
| Publication date evidence | data.mendeley.com/datasets |
| Publication date source field | Mendeley dataset version 1 page: Published |
| Publication date reviewed | 2026-09-11 |
| Primary category | fundus |
| Resource role | current_dataset |
| Dataset family | mendeley_development_deep_learning_model_epiretinal_membrane |
| Contained modalities | fundus |
| Tasks | classification |
| Primary reported quantity | Not reported |
| Classes | Not reported (Not reported) |
| Splits | all |
| Size | Not reported |
| Source-stated terms | Creative Commons Attribution 4.0 International |
| Normalized terms | cc-by |
| Descriptive screening label | Standard label without an explicit NC clause; not a permission finding |
| Terms scope | unknown |
| Access friction | self_service_authenticated |
| Route backend | Manual (upstream-gated) |
| Availability | available (checked 2026-08-02) |
| Acquisition support | guided_instructions_only |
| Legacy sample-loader status | Metadata and access only |
Reported quantities
No reproducible primary item count was exposed for the cataloged source version.
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
Human provenance: Retrospectively collected colour-fundus photographs from patients diagnosed with epiretinal membrane at B&VIIT Eye Center, Seoul. Source-review finding: Source description states retrospective CFP collection from patients with an ERM diagnosis.
Access information and download
- CLI
- Python
# Read-only preflight
eyehub download mendeley_development_deep_learning_model_epiretinal_membrane --data-dir ./data --dry-run --json
# Download, only when preflight reports supported behavior
eyehub download mendeley_development_deep_learning_model_epiretinal_membrane --data-dir ./data
from eyedatahub.acquisition import preflight_dataset
from eyedatahub.datasets.registry import REGISTRY
ds = REGISTRY.get_dataset('mendeley_development_deep_learning_model_epiretinal_membrane')
print(preflight_dataset(ds, './data')) # no download
Upstream page: data.mendeley.com/datasets
Source-term evidence: data.mendeley.com/datasets
Loader status
This catalog record provides metadata and access instructions, but it does not yet include a standard DatasetSample loader. Inspect the source file structure or contribute a loader before using it in a training pipeline.
Citation
- BibTeX
- Plain text
@misc{mendeley_development_deep_learning_model_epiretinal_membrane,
title = { Development of a deep learning model for epiretinal membrane detection in fundus photography },
note = { Development of a deep learning model for epiretinal membrane detection in fundus photography. Mendeley Data. doi:10.17632/jrgntpv8b8 },
url = { https://data.mendeley.com/datasets/jrgntpv8b8 },
}
Development of a deep learning model for epiretinal membrane detection in fundus photography. Mendeley Data. doi:10.17632/jrgntpv8b8.
Source-stated terms
- Raw source string: Creative Commons Attribution 4.0 International
- Normalized category:
cc-by - Apparent scope:
unknown - Descriptive screening label: Standard label without an explicit NC clause; not a permission finding
⚠️ 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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