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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
- [Full patient-safe dataset](https://huggingface.co/datasets/Tiendat88/FetalVision-Fetal-Planes)
- [Training source code](https://github.com/Tiendat88/FetalVision)
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](https://github.com/Tiendat88/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](https://doi.org/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.
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