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联邦学习与隐私保护为多机构的三维大脑瘤细分的联合学习.

Mohammed Elbachir Yahiaoui1, Makhlouf Derdour2, Rawad Abdulghafor3

  • 1Mathematics, Informatics and Systems LAboratory-LAMIS Laboratory, University of Echahid Cheikh Larbi Tebessi, Tebessa 12000, Algeria.

Diagnostics (Basel, Switzerland)
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PubMed
概括
此摘要是机器生成的。

这项研究介绍了一种保护隐私的联合学习模型,用于使用3D U-Net.Net准确的脑瘤细分. 该方法有效地对瘤进行细分,同时保护患者数据的机密性.

关键词:
3D U-Net 是一个 3D U-Net.大脑瘤的细分 脑瘤的细分联合学习的联合学习.维护隐私 - 维护隐私

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科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 计算生物学 计算生物学

背景情况:

  • 准确的脑瘤诊断至关重要,但深度学习的数据共享受到隐私和法律障碍的阻碍.
  • 联合学习 (FL) 提供了一个解决方案,可以在没有直接数据共享的情况下进行协作模式培训.
  • 隐私保护技术 (PPT) 对于确保数据保密至关重要.

研究的目的:

  • 实施用于脑瘤细分的联合学习方法.
  • 整合隐私保护技术 (PPT) 以提高数据保密性.
  • 为了解决医疗成像数据共享深度学习模型的挑战.

主要方法:

  • 在BraTS 2020数据集上使用3D U-Net模型与联合学习进行训练.
  • 纳入差异隐私作为PPT来保护患者数据.
  • 使用Dice相似系数 (DSC) 和95%的豪斯多夫距离 (HD95) 评估整个瘤 (WT),瘤核心 (TC) 和增强瘤核心 (ET) 的细分性能.

主要成果:

  • 联合模型在验证和测试套件上实现了具有竞争力的DSC和HD95值.
  • 在测试组中,DSC达到89.85% (WT),87.55% (TC) 和86.6% (ET),HD95值分别为22.95毫米,8.68毫米和8.32毫米.
  • 证明了细分方法及其隐私保护能力的有效性.

结论:

  • 一个配合PPT的协作联合学习模型成功地对脑瘤病变进行细分,而不会影响患者的保密性.
  • 开发的模型在脑瘤细分方面表现出很高的性能.
  • 未来的工作重点是改善模型的通用性,并将框架扩展到其他医学成像任务.