From Pretraining to Privacy: Federated Ultrasound Foundation Model with Self-Supervised Learning
Paper β’ 2411.16380 β’ Published
This is an unmodified mirror. All credit goes to the original authors.
log_2024-07-16_13_53_08/checkpoint.pth from the OneDrive folder)A privacy-preserving federated ultrasound foundation model, collaboratively pre-trained with self-supervised learning (MAE) across 16 institutions without sharing raw data β ~1M images, 19 organs, 10 modalities β for downstream diagnosis and segmentation.
| File | Purpose |
|---|---|
checkpoint.pth (1.34 GB) |
Pre-trained UltraFedFM checkpoint β place in output_dir/ per the upstream README |
LICENSE |
Upstream Apache-2.0 license text |
Apache-2.0, as released by the original authors. See LICENSE.
@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}
}