OCTDL Real: Optical Coherence Tomography Images with 5-Fold Patient Split

OCTDL Real 5-Fold Patient Split is a dataset from Kaggle. The title suggests it contains Optical Coherence Tomography (OCT) images organized for machine learning, likely with a predefined cross-validation split based on patient identity to prevent data leakage. The dataset's author, organization, size, and specific contents are not provided in the metadata.

Use Cases

• Train a classifier for retinal disease detection from OCT scans (inferred from domain, verify after download)
• Benchmark model performance using a standardized patient-wise cross-validation split (inferred from domain, verify after download)
• Develop image segmentation models for retinal layers or pathologies (inferred from domain, verify after download)

Strengths

• Published on Kaggle, a platform with an active community for data science.
• The title indicates a structured 5-fold split designed to separate patient data, which is a good practice for medical ML evaluation.

Limitations

• Metadata is minimal; actual content requires verification after download.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.

Provenance

Source

Kaggle

Collection Method

Unknown

Time Range

Unknown

Freshness

Last update date is unknown; freshness unverified.

Geography

Unknown