联合学习的医学成像应用 联合学习的应用
Sukhveer Singh Sandhu1, Hamed Taheri Gorji1,2, Pantea Tavakolian1
1Biomedical Engineering Program, University of North Dakota, Grand Forks, ND 58202, USA.
Diagnostics (Basel, Switzerland)
|October 14, 2023
概括
联合学习 (FL) 通过提供隐私和安全来增强医学成像分析. 本综述详细介绍了FL在医学成像中的应用,强调了其潜力和挑战.
科学领域:
- 人工智能的人工智能
- 医疗成像医学成像
- 计算机科学 计算机科学
背景情况:
- 联合学习 (FL) 提供了固有的安全性,隐私和可扩展性优势.
- FL能够克服数据偏差的能力使其适用于敏感的医疗保健数据集.
- 现有的审查广泛涵盖FL,但需要对医学成像应用进行专注的审查.
研究的目的:
- 系统地审查医学成像中的联合学习应用程序.
- 根据疾病,成像方式和解剖部位对FL应用进行分类.
- 对传统的机器学习模型进行FL模型性能分析.
主要方法:
- 对ArXiv,IEEE Xplorer和PubMed的系统文献审查.
- 在医学成像中使用的FL架构和模型的详细描述.
- 对FL和传统机器学习性能进行比较分析.
主要成果:
- 飞行器越来越多地应用于各种医学成像任务,显示出有希望的结果.
- 隐私保护技术,如同态加密和差异隐私,是 FL 的关键组成部分.
- FL模型的性能与传统的ML相美,并且在不断改进中.
结论:
- 联合学习显示了安全和私人医疗成像分析的巨大潜力.
- 与安全和数据相关的挑战仍然是FL在医疗保健中的首要问题.
- 进一步的研究和开发对于在医学成像中推进FL至关重要.
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