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About Dataset
This dataset contains metadata extracted from training DICOM image files used in the RSNA 2022 Cervical Spine Fracture Detection competition.
Files Included
- meta-train: Original extracted metadata (use with caution)
- meta-train-clean: Cleaned version of
meta-train, easier to use - meta-segmentations: Metadata for images with segmentations, includes correct vertebra labels (C1–C7) extracted from segmentation masks
- meta-segmentation-clean: Cleaned version of
meta-segmentations - meta-train-with-vertebrae: Metadata for all training images, includes 88% accurate Random Forest predictions of the vertebra in each image
- train-segmented: Metadata for all train images with 95% accurate EffNetV2 vertebrae predictions (based on provided notebook)
- train-vert-fold4: Version of
train-segmentedcleaned and enriched via an image+tabular model; includes extra feature-engineered columns - train-vert: Final ensembled predictions from
train-segmentedandtrain-vert-fold4
Related Notebooks
- RSNA Fracture Detection – In-depth EDA
- Extracting Vertebrae C1, ..., C7