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Tiendat88/
FetalVision

Name: FetalVision
Availability: InStock

out-of-distribution-detection

autoencoder

image-classification

other

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FetalVision

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PyTorch hybrid autoencoder + latent classifier for detecting non-standard/OOD fetal ultrasound views. The production ensemble combines three independently trained seeds.

Results

Model	ROC-AUC	AP	F1	Accuracy
Best single model (seed 2026)	0.9754	0.9630	0.8782	0.9132
Three-seed ensemble	0.9796	0.9680	0.8887	0.9219

The ensemble uses weights 0.30 / 0.35 / 0.35 for seeds 42 / 1337 / 2026, selected only on the validation set.

Files

models/seed42/best_model.pt
models/seed1337/best_model.pt
models/seed2026/best_model.pt
ensemble/summary.json
Per-run summaries under each model directory
Training and score-distribution plots for the best single run

Architecture and preprocessing configuration are stored inside every checkpoint and summary file. Full source code and reproducible training commands are available in GitHub FetalVision.

Intended use and limitations

Other is a proxy for non-standard/out-of-distribution views. It is not a fetal pathology label, and this model must not be interpreted as a diagnostic system. Clinical deployment requires external validation, calibration and appropriate regulatory review.

Dataset attribution

Trained using FETAL_PLANES_DB by Burgos-Artizzu et al., DOI: 10.5281/zenodo.3904280. The source record does not state a machine-readable license, so this model card uses license: other rather than inventing a license.

Tiendat88/FetalVision