| --- |
| 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} |
| } |
| ``` |
|
|
