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From pretraining to privacy: federated ultrasound foundation model with self-supervised learning

文献类型：期刊论文

作者Jiang, Yuncheng3,4,5,6,7,8; Feng, Chun-Mei9; Ren, Jinke3,4; Wei, Jun10; Zhang, Zixun3,4; Hu, Yiwen11; Liu, Yunbi2; Sun, Rui3,4; Tang, Xuemei18; Du, Juan19
刊名NPJ DIGITAL MEDICINE
出版日期2025-11-21
卷号8期号:1页码:18
ISSN号2398-6352
DOI10.1038/s41746-025-02085-0
英文摘要Ultrasound imaging is widely used in clinical diagnosis due to its non-invasive nature and real-time capabilities. However, traditional ultrasound diagnostics relies heavily on physician expertise and is often hampered by suboptimal image quality, leading to potential diagnostic errors. While artificial intelligence (AI) offers a promising solution to enhance clinical diagnosis by detecting abnormalities across various imaging modalities, existing AI methods for ultrasound face two major challenges. First, they typically require vast amounts of labeled medical data, raising serious concerns regarding patient privacy. Second, most models are designed for specific tasks, which restricts their broader clinical utility. To overcome these challenges, we present UltraFedFM, an innovative privacy-preserving ultrasound foundation model. UltraFedFM is collaboratively pre-trained using federated learning across 16 distributed medical institutions in 9 countries, leveraging a dataset of over 1 million ultrasound images covering 19 organs and 10 ultrasound modalities. This extensive and diverse data, combined with a secure training framework, enables UltraFedFM to exhibit strong generalization and diagnostic capabilities. It achieves an average area under the receiver operating characteristic curve (AUROC) of 0.927 for disease diagnosis and a dice similarity coefficient (DSC) of 0.878 for lesion segmentation. Notably, UltraFedFM surpasses the diagnostic accuracy of mid-level ultrasonographers (4-8 years of experience) and matches the performance of expert-level sonographers (10+ years of experience) in the joint diagnosis of 8 common systemic diseases.c These findings indicate that UltraFedFM can significantly enhance clinical diagnostics while safeguarding patient privacy, marking a significant advancement in AI-driven ultrasound imaging for future clinical applications.
资助项目Guangdong Research Project[2017ZT07X152] ; Shenzhen Outstanding Talents Training Fund[202002] ; Shenzhen Key Laboratory of Big Data and Artificial intelligence[ZDSYS201707251409055] ; Key Area R&D Program of Guangdong Province[2018B030338001] ; Guangdong Provincial Key Laboratory of Future Networks of Intelligence[2022B1212010001] ; National Natural Science Foundation of China[62293482] ; National Natural Science Foundation of China[61931024] ; Shenzhen General Program[JCYJ20220530143600001] ; Basic Research Project of Hetao Shenzhen HK S&T Cooperation Zone[HZQB-KCZYZ-2021067] ; Guangdong Provincial Key Laboratory of BigData Computing CHUK-Shenzhen ; China Association for Science and Technology Youth Care Program ; Tencent & Huawei Open Fund ; Shenzhen-Hong Kong Joint Funding[SGDX20211123112401002]
WOS研究方向Health Care Sciences & Services ; Medical Informatics
语种英语
WOS记录号WOS:001620847800004
出版者NATURE PORTFOLIO
源URL[http://119.78.100.204/handle/2XEOYT63/43071]  
专题中国科学院计算技术研究所
通讯作者Li, Zhen
作者单位1.Harbin Inst Technol, Biocomp Res Ctr, Shenzhen 518055, Peoples R China
2.Nanjing Univ Posts & Telecommun, Sch Comp Sci, Nanjing 210023, Peoples R China
3.Chinese Univ Hong Kong, FNii Shenzhen, Shenzhen 518172, Peoples R China
4.Chinese Univ Hong Kong, Sch Sci & Engn, Shenzhen 518172, Peoples R China
5.Sichuan Univ, West China Hosp, Dept Gen Surg, Chengdu, Peoples R China
6.Sichuan Univ, Collaborat Innovat Ctr Biotherapy, Lab Gastr Canc, State Key Lab Biotherapy, Chengdu, Peoples R China
7.Sichuan Univ, West China Hosp, Canc Ctr, Chengdu, Peoples R China
8.Sichuan Univ, West China Hosp, Gastr Canc Ctr, Chengdu, Peoples R China
9.Univ Coll Dublin, Sch Comp Sci, Dublin, Ireland
10.Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen 518060, Peoples R China
推荐引用方式
GB/T 7714		 Jiang, Yuncheng,Feng, Chun-Mei,Ren, Jinke,et al. From pretraining to privacy: federated ultrasound foundation model with self-supervised learning[J]. NPJ DIGITAL MEDICINE,2025,8(1):18.
APA Jiang, Yuncheng.,Feng, Chun-Mei.,Ren, Jinke.,Wei, Jun.,Zhang, Zixun.,...&Li, Zhen.(2025).From pretraining to privacy: federated ultrasound foundation model with self-supervised learning.NPJ DIGITAL MEDICINE,8(1),18.
MLA Jiang, Yuncheng,et al."From pretraining to privacy: federated ultrasound foundation model with self-supervised learning".NPJ DIGITAL MEDICINE 8.1(2025):18.

入库方式： OAI收割

来源：计算技术研究所

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