From pretraining to privacy: federated ultrasound foundation model with self-supervised learning.
Authors
Affiliations (20)
Affiliations (20)
- FNii-Shenzhen, The Chinese University of Hong Kong, Shenzhen, Shenzhen, 518172, China.
- School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, Shenzhen, 518172, China.
- Department of General Surgery & Laboratory of Gastric Cancer, State Key Laboratory of Biotherapy, Collaborative Innovation Center of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, China.
- Gastric Cancer Center, West China Hospital, Sichuan University, Chengdu, China.
- School of Computer Science, University College Dublin, Dublin, Ireland.
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China.
- South China Hospital, Health Science Center, Shenzhen University, Shenzhen, 518111, China.
- School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China.
- Affiliated Hospital of North Sichuan Medical College, Sichuan, 637000, China.
- North Sichuan Medical College, Sichuan, 637000, China.
- Shenzhen Research Institute of Big Data, Shenzhen, 518172, China.
- Bio-Computing Research Center, Harbin Institute of Technology, Shenzhen, Shenzhen, 518055, China.
- School of Computer Science, Wuhan University, Wuhan, 430072, China.
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia.
- Center of Excellence for Smart Health (KCSH), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia.
- Beijing University of Posts and Telecommunications, Beijing, 100876, China.
- School of Biomedical Engineering, Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, 215123, China.
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China.
- FNii-Shenzhen, The Chinese University of Hong Kong, Shenzhen, Shenzhen, 518172, China. [email protected].
- School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, Shenzhen, 518172, China. [email protected].
Abstract
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.