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放射学中的联合学习:关于医学图像分析的全面元调查

Asaf Raza¹, Antonella Guzzo¹, Michele Ianni¹

¹Department of Informatics, Modeling, Electronics, and Systems, University of Calabria, Rende, Italy.

Computer methods and programs in biomedicine

|April 25, 2025

概括

医学成像中的联合学习 (FL) 能够在保护患者数据的同时进行协作分析. 本次元分析回顾了FL-MI趋势,数据异质性等挑战,以及未来对强大隐私的研究需求.

科学领域:

医疗成像医学成像
人工智能的人工智能
数据隐私数据隐私数据

背景情况:

联合学习 (FL) 为协作医疗图像分析提供了一种保护隐私的方法.
由于敏感的患者数据,放射学任务从FL中获得了显著的好处.

研究的目的:

系统地对最近关于医学成像学联合学习 (FL-MI) 的调查进行元分析.
确定过去五年FL-MI研究中的趋势,挑战和战略.

主要方法:

采用PRISMA方法论进行系统审查和分析.
来自知名场所的现有FL-MI研究的分类和分析.

主要成果:

确定了FL-MI.MI.中的共同趋势,挑战 (数据异质性,隐私,非IID设置) 和新兴策略.
突出了广泛使用的数据集,机器学习模型和FL框架,用于瘤检测和细分等应用.
确定了研究缺陷,特别是需要加强隐私保护的需要.

结论:

医学成像中的联合学习是一个快速发展的领域,具有巨大的潜力.
进一步的研究对于应对当前的挑战和推进保护隐私的协作医学图像分析至关重要.

关键词:

联合学习学习 (Federated Learning) 是一种联合学习.医学成像医学成像这是一项超级调查 (Meta-Survey).无线电学 (Radiomics) 是一种辐射学.

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