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Focus-weighted, machine learning disease classifier error prediction for microscope slide images

机译:聚焦加权,机器学习疾病分类器显微镜幻灯片图像误差预测

摘要

A method is described for generating a prediction of a disease classification error for a magnified, digital microscope slide image of a tissue sample. The image is composed of a multitude of patches or tiles of pixel image data. An out-of-focus degree per patch is computed using a machine learning out-of-focus classifier. Data representing expected disease classifier error statistics of a machine learning disease classifier for a plurality of out-of-focus degrees is retrieved. A mapping of the expected disease classifier error statistics to each of the patches of the digital microscope slide image based on the computed out-of-focus degree per patch is computed, thereby generating a disease classifier error prediction for each of the patches. The disease classifier error predictions thus generated are aggregated over all of the patches.
机译:描述了一种用于生成组织样本的放大的数字显微镜滑动图像的疾病分类误差的预测。 图像由像素图像数据的多种斑块或瓦片组成。 使用机器学习超细分类器计算每个补充的焦点度。 检索代表预期疾病分类器的数据,用于多个焦点间度的机器学习疾病分类器的误差统计。 计算预期的疾病分类器误差统计的数码显微镜滑动图像的每个贴片,基于每个补丁的计算的焦点,从而为每个贴片产生疾病分类器误差预测。 由此生成的疾病分类器误差预测在所有补丁上聚合。

著录项

  • 公开/公告号US11164048B2

    专利类型

  • 公开/公告日2021-11-02

    原文格式PDF

  • 申请/专利权人 GOOGLE LLC;

    申请/专利号US202016883014

  • 发明设计人 MARTIN STUMPE;TIMO KOHLBERGER;

    申请日2020-05-26

  • 分类号G06K9;G06K9/62;G16H30/40;G06N20;G06K9/03;G06N3/08;

  • 国家 US

  • 入库时间 2022-08-24 22:01:53

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