Yiwen Wang , Wanli Ding , Weiyuan Lin , Tao Tan , Zhifan Gao
{"title":"FedHNR: Federated hierarchical resilient learning for echocardiogram segmentation with annotation noise","authors":"Yiwen Wang , Wanli Ding , Weiyuan Lin , Tao Tan , Zhifan Gao","doi":"10.1016/j.eswa.2025.126841","DOIUrl":null,"url":null,"abstract":"<div><div>Echocardiogram segmentation based on federated learning plays a critical role in enhancing diagnostic accuracy and efficiency. However, challenges such as inter-client annotation noise, client heterogeneity, and limited expert annotations hinder the echocardiogram segmentation based on federated learning. To address these challenges, we propose FedHNR, a federated hierarchical noise-resilient method that identifies and leverages annotation noise across global and local hierarchies. At the global-hierarchy, expert samples fine-tune the global model through a novel weight noise decoupling approach, reducing overfitting while preserving aggregated client knowledge. At the local-hierarchy, FedHNR employs region-level noise assessment and sample-level noise calibration to refine annotations using pseudo-clean labels derived from the global model. These hierarchies together mitigate the negativeness of noise and enhance the model robustness to noise. Extensive experiments on 95,469 echocardiogram frames across public and private datasets demonstrate that FedHNR outperforms ten state-of-the-art methods, showcasing its robustness in both traditional federated learning and real-world scenarios.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"273 ","pages":"Article 126841"},"PeriodicalIF":7.5000,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Expert Systems with Applications","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0957417425004634","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
FedHNR: Federated hierarchical resilient learning for echocardiogram segmentation with annotation noise
Echocardiogram segmentation based on federated learning plays a critical role in enhancing diagnostic accuracy and efficiency. However, challenges such as inter-client annotation noise, client heterogeneity, and limited expert annotations hinder the echocardiogram segmentation based on federated learning. To address these challenges, we propose FedHNR, a federated hierarchical noise-resilient method that identifies and leverages annotation noise across global and local hierarchies. At the global-hierarchy, expert samples fine-tune the global model through a novel weight noise decoupling approach, reducing overfitting while preserving aggregated client knowledge. At the local-hierarchy, FedHNR employs region-level noise assessment and sample-level noise calibration to refine annotations using pseudo-clean labels derived from the global model. These hierarchies together mitigate the negativeness of noise and enhance the model robustness to noise. Extensive experiments on 95,469 echocardiogram frames across public and private datasets demonstrate that FedHNR outperforms ten state-of-the-art methods, showcasing its robustness in both traditional federated learning and real-world scenarios.
期刊介绍:
Expert Systems With Applications is an international journal dedicated to the exchange of information on expert and intelligent systems used globally in industry, government, and universities. The journal emphasizes original papers covering the design, development, testing, implementation, and management of these systems, offering practical guidelines. It spans various sectors such as finance, engineering, marketing, law, project management, information management, medicine, and more. The journal also welcomes papers on multi-agent systems, knowledge management, neural networks, knowledge discovery, data mining, and other related areas, excluding applications to military/defense systems.