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路径FL:多对齐的联合学习,用于病理学图像细分.

Yuan Zhang1, Feng Chen2, Yaolei Qi1

  • 1Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, No. 2, Sipai Lou, Xuanwu District, Nanjing, 210096, China.

Medical image analysis
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概括
此摘要是机器生成的。

一个新的联合学习框架PathFL通过在不同数据集中对齐数据,特征和模型来增强病理图像细分. 这种方法提高了对成像和设备异质性的概括性和稳定性.

关键词:
联合学习是联合学习.异质性 异质性 异质性病理学图像图像 病理学图像分段化 分段化 分段化 分段化

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科学领域:

  • 数字病理学数字病理学
  • 医学图像分析 医学图像分析
  • 人工智能的人工智能

背景情况:

  • 由于来自各种来源的数据异质性,例如成像模式和设备,病理图像细分面临着挑战.
  • 这种异质性导致了表示偏差,阻碍了可概括的细分模型的开发.

研究的目的:

  • 提出PathFL,一个多对齐的联合学习 (FL) 框架,用于强大的病理图像细分.
  • 解决数据异质性问题,并提高细分模型在各种病理成像场景中的通用性.

主要方法:

  • 路径FL采用了三级对齐策略:图像级协作风格增强,特征级自适应特征对齐和模型级分层相似性聚合.
  • 图像级对齐方便了风格信息交换的数据多样化.
  • 特征级别对齐将本地特征与全球洞察力相结合,以实现表示一致性.
  • 模型级聚合使用层特定的相似性来解释客户端差异.

主要成果:

  • 在四个异质病理图像数据集中,PathFL表现出卓越的性能和稳定性.
  • 评估包括跨源,跨模式,跨器官和跨扫描器的变化.
  • 该框架有效地减轻了由数据异质性引起的表示偏差.

结论:

  • 在异质的多中心环境中,PathFL为病理图像细分提供了有效的解决方案.
  • 拟议的多对齐策略显著提高了对不同数据变异的模型概括性和稳定性.
  • 该框架显示了在数字病理学中开发可靠的AI工具的前景.