IF 7.5 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Expert Systems with Applications Pub Date : 2025-02-18 DOI:10.1016/j.eswa.2025.126841
Yiwen Wang , Wanli Ding , Weiyuan Lin , Tao Tan , Zhifan Gao
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引用次数: 0

摘要

基于联合学习的超声心动图分割在提高诊断准确性和效率方面起着至关重要的作用。然而,客户端间注释噪声、客户端异质性和有限的专家注释等挑战阻碍了基于联合学习的超声心动图分割。为了应对这些挑战,我们提出了一种联合分层抗噪方法 FedHNR,它能识别并利用全局和局部分层的注释噪声。在全局层次结构中,专家样本通过一种新颖的权重噪声解耦方法对全局模型进行微调,从而减少过拟合,同时保留聚合的客户知识。在局部层次上,FedHNR 采用区域级噪声评估和样本级噪声校准,利用从全局模型中提取的伪清洁标签完善注释。这些层次结构共同减轻了噪声的负面影响,增强了模型对噪声的鲁棒性。在公共和私有数据集的 95,469 个超声心动图帧上进行的广泛实验表明,FedHNR 优于十种最先进的方法,展示了它在传统联合学习和现实世界场景中的鲁棒性。
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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.
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来源期刊
Expert Systems with Applications
Expert Systems with Applications 工程技术-工程:电子与电气
CiteScore
13.80
自引率
10.60%
发文量
2045
审稿时长
8.7 months
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.
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