Application progress of federated learning of artificial intelligence in ultrasound medicine
10.3760/cma.j.cn131148-20250401-00180
- VernacularTitle:联邦学习在超声医学人工智能领域中的应用进展
- Author:
Qi YANG
1
;
Tingyang YANG
;
Jiancheng HAN
;
Yihua HE
Author Information
1. 首都医科大学附属北京安贞医院，心脏超声医学中心，北京 100029
- Publication Type:Journal Article
- Keywords:
Federated learning;
Ultrasonic medicine;
Artificial intelligence
- From:
Chinese Journal of Ultrasonography
2025;34(9):766-770
- CountryChina
- Language:Chinese
-
Abstract:
Ultrasound medicine is crucial to assist clinical diagnosis and treatment. The application of artificial intelligence in ultrasound medicine has received extensive attention to assist in clinical diagnosis and improve diagnostic accuracy and prognosis. However，the generalization of existing models is limited by small sample size，data heterogeneity，and patient privacy protection. Federated learning，as a distributed learning paradigm，enables multiple centers to conduct local training and aggregate model parameters to jointly train a global model，effectively increasing the sample size and data diversity without exchanging raw data，thereby protecting patient privacy. This approach has promising clinical application prospects. However，there are still challenges in optimizing the defense capability，performance，and diverse applicability of the model. This article reviews the application and challenges of federated learning in ultrasound image analysis and diseases diagnosis.
