library_name: pytorch
pipeline_tag: image-classification
tags:
- medical-imaging
- ultrasound
- fetal-ultrasound
- anomaly-detection
- out-of-distribution-detection
- autoencoder
license: other
FetalVision
Quick links
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.ptmodels/seed1337/best_model.ptmodels/seed2026/best_model.ptensemble/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.