View in EDS

Interpretable Semi-federated Learning for Multimodal Cardiac Imaging and Risk Stratification: A Privacy-Preserving Framework.

Saved in:
Bibliographic Details
Title: Interpretable Semi-federated Learning for Multimodal Cardiac Imaging and Risk Stratification: A Privacy-Preserving Framework.
Authors: Liu X; Heart Center, Department of Cardiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, 310014, Zhejiang, China., Li S; Multi-Scale Medical Robotics Centre, Ltd, The Chinese University of Hong Kong, Hong Kong, China., Zhu Q; Department of Nephrology, Hangzhou TCM Hospital Affiliated With Zhejiang Chinese Medical University, No. 453, Stadium Road, Xihu District, Hangzhou, 310007, Zhejiang Province, China., Xu S; Heart Center, Department of Geriatrics, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, 310014, Zhejiang, China., Jin Q; Heart Center, Department of Geriatrics, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, 310014, Zhejiang, China. jinqinyang@hmc.edu.cn.
Source: Journal of imaging informatics in medicine [J Imaging Inform Med] 2026 Jun; Vol. 39 (3), pp. 2641-2660. Date of Electronic Publication: 2025 Sep 05.
Publication Type: Journal Article
Journal Info: Publisher: Springer Nature Country of Publication: Switzerland NLM ID: 9918663679206676 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2948-2933 (Electronic) Linking ISSN: 29482925 NLM ISO Abbreviation: J Imaging Inform Med Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2948-2933
DOI:10.1007/s10278-025-01643-y