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Updated: Jun 23, 2025

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在皮肤损伤分类中的有效注释策略的联合主动学习框架.

Zhipeng Deng1, Yuqiao Yang1, Kenji Suzuki1

  • 1Biomedical Artificial Intelligence Research Unit (BMAI), Institute of Innovative Research, Tokyo Institute of Technology, Tokyo, Japan; Department of Information and Communications Engineering, School of Engineering, Tokyo Institute of Technology, Tokyo, Japan.

The Journal of investigative dermatology
|June 23, 2024
PubMed
概括

联合学习与主动学习相结合,大大减少了医疗成像数据注释需求. 这种方法实现了最先进的性能,同时保护了患者的隐私并降低了注释成本.

关键词:
积极学习是指积极学习.联合学习是联合学习.人在循环中的机器学习医学成像医学成像皮肤病变 皮肤病变

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科学领域:

  • 人工智能的人工智能
  • 医疗成像医学成像
  • 机器学习 机器学习

背景情况:

  • 联合学习 (FL) 允许在没有数据共享的情况下进行协作模型培训,这对于隐私敏感的医疗数据至关重要.
  • 医学成像中的数据注释是劳动密集型的,需要专业知识,这对FL来说是一个重大挑战.
  • 积极学习 (AL) 方法可以通过选择信息样本来减少注释负担.

研究的目的:

  • 为医疗图像分析提出一个新的联合主动学习 (AL) 框架.
  • 在联合学习场景中解决密集数据注释要求的关键问题.
  • 为了减少所需的注释数据的数量,同时保持高模型性能和患者隐私.

主要方法:

  • 开发了一个联合的AL框架,定期和交互地在FL过程中整合AL.
  • 利用FL的局部和全球模型组合来进行数据选择.
  • 作为一个高效的数据注释策略,采用基于集体的AL.

主要成果:

  • 联合AL框架在使用仅50%的数据实现了皮肤损伤分类任务的最先进性能.
  • 在FL下超越了几种最先进的AL方法.
  • 证明了与完整数据FL可比的性能,同时显著减少了注释工作和保护隐私.

结论:

  • 拟议的联合AL框架有效地减少了医疗成像FL中的数据注释要求.
  • 这种方法保持了高性能和患者隐私,为医疗AI开发提供了实用解决方案.
  • 这代表了联合AL对医学图像的新应用,在真实世界皮肤镜数据集上得到验证.