---
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}
}
```