在协作学习框架中,一种联合学习方法来预测乳腺癌
Maram Fahaad Almufareh1, Noshina Tariq2, Mamoona Humayun1
1Department of Information Systems, College of Computer and Information Sciences, Jouf University, Al Jouf 72311, Saudi Arabia.
Healthcare (Basel, Switzerland)
|December 22, 2023
概括
使用联邦学习 (FL) 的新深度神经网络 (DNN) 模型在乳腺癌检测中实现了97%的准确性. 这种协作方法提高了全球的早期诊断和患者结果.
科学领域:
- 在瘤学瘤学.
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 乳腺癌是一个重要的全球健康问题,需要先进的诊断工具.
- 及时检测和管理对于改善患者的治疗结果至关重要.
- 现有的诊断方法可以通过利用人工智能和协作数据方法来增强.
研究的目的:
- 利用深度神经网络 (DNN) 开发一种高精度的乳腺癌检测模型.
- 实施一个联邦学习 (FL) 框架,在多个机构中提供安全,协作模式的培训.
- 提高乳腺癌查的准确性和可靠性.
主要方法:
- 用DNNs进行协作学习的代方法.
- 雇佣联合学习 (FL) 来汇总来自不同医疗保健数据源的知识,同时保护患者的隐私.
- 应用最佳特征选择和数据增强技术,以提高模型性能.
主要成果:
- 在乳腺癌检测中达到97.54%的最大精度.
- 报告了96.5%的精度和98.0%的召回率.
- 获得了97%的F1评分,表明模型性能强.
结论:
- 与FL一起开发的DNN模型代表了乳腺癌诊断的重大进步.
- 联合学习提供了一种安全有效的方法,可以利用多机构数据来改进人工智能模型.
- 这种方法有可能改变乳腺癌的早期检测和治疗,使全世界的患者受益.
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