Ontology-Grounded Structured Prediction for Dental CBCT Reporting

Code for the MICCAI paper "Ontology-Grounded Structured Prediction for Dental CBCT Reporting".

Contributions

A dental CBCT ontology for structured report labels: ontology/ontology.ttl.
A conversion pipeline from raw Italian reports to patient-level TTL labels.
An ontology-driven prediction model that adapts its heads to the ontology schema.

Put raw reports in data/ToothFairy3/reportsTr/.
Convert reports to structured TTL with the main script, ontology/extract.py:
```
OPENAI_API_KEY=... python ontology/extract.py
```
This combines all reports for each patient into one TTL. ontology/extract_single.py is only for one-report-per-TTL inter-agreement checks.
Check outputs:
- valid TTLs: data/ttl_reports/
- generated single-report TTLs: data/ttl_reports_single/
- SHACL/API errors: data/shacl_errors/
- schema files: ontology/ontology.ttl, ontology/shapes.ttl

Preprocess CBCT volumes into regional tokens:

python main.py preprocess --dataset-root data/ToothFairy3 --output-dir data/preprocessed_tokens

Train:

python -m src.train --wandb_name experiment_name

For a quick local run:

python -m src.train --use_kfold false --max_epochs 5 --no_wandb

TTL filenames must match preprocessed token patient IDs.
Training only uses samples with both .pt tokens and .ttl labels.
Inspect label distribution before training:
```
python -m src.scripts.histogram_ttl
```