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SeLa-MIL:通过弱监督的自我培训开发实例级分类器,用于整个幻灯片图像分类.

Yingfan Ma1, Mingzhi Yuan1, Ao Shen1

  • 1Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, 200032, China; Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Fudan University, Shanghai, 200032, China.

Computer methods and programs in biomedicine
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概括
此摘要是机器生成的。

通过使用半监督学习来利用标记和未标记的数据,SeLa-MIL增强了病理图像分类,提高了难以诊断癌症的准确性. 这种方法在识别整个幻灯片图像 (WSIs) 中的关键积极实例方面表现出色.

关键词:
计算病理学计算病理学多个实例的学习是多个实例的学习.监督的弱点 监督的弱点整个幻灯片图像的分类整体幻灯片图像的分类.

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科学领域:

  • 计算病理学计算病理学
  • 机器学习用于医学成像.
  • 癌症诊断中的人工智能

背景情况:

  • 全幻灯片图像 (WSI) 分类对于癌症诊断至关重要,但由于注释成本,通常使用多个实例学习 (MIL).
  • 现有的MIL方法通过专注于袋级分类和忽视实例级数据而扎于低于最佳的结果.
  • 接近决策边界的具有挑战性的积极实例的准确分类仍然是一个重大障碍.

研究的目的:

  • 引入SeLa-MIL,这是WSI分类的新型半监督学习方法.
  • 通过使用标记和未标记的实例来提高实例级和袋级分类准确性.
  • 为了特别提高在病理学图像中确定硬阳性实例.

主要方法:

  • 重构了MIL作为一个半监督的实例分类任务,以整合标记和未标记的数据.
  • 开发了一个弱监督的自我训练框架,使用一个受约束的优化问题来处理所有负标记实例.
  • 在假标签上使用全球和本地约束,这些约束来自积极的WSI信息,以改善硬积极实例学习.

主要成果:

  • 在合成,MIL基准和WSI数据集上,SeLa-MIL在现有方法上表现优越.
  • 在实例级和袋级分类准确度方面取得了实质性的改进.
  • 通过可视化,突出显示癌症诊断的相关病理区域,进一步验证了有效性.

结论:

  • 通过半监督学习,弱监督学习和伪标签,SeLa-MIL成功解决了WSI分类中的MIL挑战.
  • 该方法提高了不同数据集的分类准确性和概括能力.
  • 为推进病理图像分析和计算机辅助诊断提供了一个有价值的工具.