计算病理学中的联合学习:文献综述
1University at Buffalo SUNY, Department of Pathology and Anatomical Sciences, Buffalo, New York, United States.
Journal of medical imaging (Bellingham, Wash.)
|November 28, 2025
概括
联合学习 (FL) 能够在医疗保健中进行协作AI模型培训,而无需共享敏感数据,显示了计算病理学的前景. 标准化和异质性的挑战仍然存在,但正在为未来的临床整合解决.
科学领域:
- 医疗保健中的人工智能
- 计算病理学和医学成像学
- 分散的机器学习技术分散的机器学习技术.
背景情况:
- 集中式人工智能模型由于数据聚合而危及患者隐私.
- 联合学习 (FL) 为协作模式培训提供了一种保护隐私的替代方案.
- 对于敏感的医疗数据和计算病理学来说,FL特别重要.
研究的目的:
- 系统地审查FL在医疗保健中的最先进应用.
- 专注于FL在计算病理学和医学成像中的作用.
- 识别医疗保健的动机,战略,挑战和解决方案.
主要方法:
- 在医疗保健中对FL进行系统的文献审查,强调计算病理学.
- 分析使用多种医学成像方法 (组织病理学,MRI,CT,PET) 的研究.
- 根据FL架构,动机,实施和挑战对研究进行分类.
主要成果:
- 在医疗保健中越来越多地采用FL,在计算病理学中越来越多地使用FL.
- FL模型的准确性与集中式模型的准确性相当或更高,同时保持隐私.
- 联合培训是可行的计算病理学任务,如预测和细分.
结论:
- FL显示出安全,保护隐私的医疗保健合作的巨大潜力,特别是在计算病理学领域.
- 关键的挑战包括数据异质性,系统互操作性和模型可解释性.
- 未来的研究需要专注于标准化,稳定性和临床整合的伦理考虑.
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