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深度学习量化病理学家的视觉模式,用于整个幻灯片图像诊断.

Tianhang Nan1, Song Zheng2,3, Siyuan Qiao4

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科学领域:

  • 数字病理学数字病理学
  • 医学中的人工智能.
  • 计算病理学计算病理学

背景情况:

  • 对整个幻灯片图像 (WSI) 的像素手动注释对于训练诊断病理学的深度学习模型至关重要.
  • 目前的注释方法耗时,给病理学家带来负担,并限制数据集大小和模型精度.

研究的目的:

  • 开发一种方法,以最小的工作量获得病理学家的专业知识.
  • 设计一个深度学习系统,解码视觉模式,用于精确的WSI诊断.

主要方法:

  • 采集了病理学家使用眼睛跟踪设备的图像审查模式.
  • 开发了病理学专业知识获取网络 (PEAN),这是一个使用视觉模式的深度学习系统.
  • 在5个皮肤病变类别的5881个世界卫生组织上评估了PEAN.

主要成果:

  • 眼睛追踪将WSI注释时间缩短到手动注释的4%.
  • 在诊断预测中,PEAN实现了0.992和96.3%的准确度的曲线下的区域.
  • 该系统有效地从病理学家的诊断过程中学习.

结论:

  • PEAN提供了高效的数据注释,大大减少了病理学家的工作量.
  • 该系统可以进行精确的诊断,帮助大规模数据收集和临床护理.
  • 这种方法通过结合病理学家的诊断过程来解决当前模型的局限性.