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license: apache-2.0
tags:
- ultrasound
- medical-imaging
- foundation-model
- federated-learning
- self-supervised-learning
- masked-autoencoder
library_name: pytorch
---
# UltraFedFM — Federated Ultrasound Foundation Model (mirror)
**This is an unmodified mirror.** All credit goes to the original authors.
- Original code: https://github.com/yuncheng97/UltraFedFM
- Original weights: OneDrive / Google Drive / Baidu links in the upstream README
(this is `log_2024-07-16_13_53_08/checkpoint.pth` from the OneDrive folder)
- Paper: [npj Digital Medicine 8, 714 (2025)](https://arxiv.org/abs/2411.16380)
A privacy-preserving federated ultrasound foundation model, collaboratively
pre-trained with self-supervised learning (MAE) across 16 institutions without
sharing raw data — ~1M images, 19 organs, 10 modalities — for downstream
diagnosis and segmentation.
## Files
| File | Purpose |
|---|---|
| `checkpoint.pth` (1.34 GB) | Pre-trained UltraFedFM checkpoint — place in `output_dir/` per the upstream README |
| `LICENSE` | Upstream Apache-2.0 license text |
## License
**Apache-2.0**, as released by the original authors. See `LICENSE`.
## Citation
```bibtex
@article{jiang2025pretraining,
title = {From pretraining to privacy: federated ultrasound foundation model with self-supervised learning},
author = {Jiang, Yuncheng and Feng, Chun-Mei and Ren, Jinke and Wei, Jun and Zhang, Zixun and Hu, Yiwen and Liu, Yunbi and Sun, Rui and Tang, Xuemei and Du, Juan and others},
journal = {npj Digital Medicine},
volume = {8},
number = {1},
pages = {714},
year = {2025},
publisher = {Nature Publishing Group UK London}
}
```
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