公开(公告)号：US20200285908A1

公开(公告)日：2020-09-10

申请号：US16883014

申请日：2020-05-26

Applicant: Google LLC

Inventor： Martin Stumpe ,  Timo Kohlberger

IPC: G06K9/62 ,  G16H30/40 ,  G06N20/00 ,  G06K9/00 ,  G06K9/03 ,  G06N3/08

Abstract: 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.

公开(公告)号：US10706328B2

公开(公告)日：2020-07-07

申请号：US15972929

申请日：2018-05-07

Applicant: Google LLC

Inventor： Martin Stumpe ,  Timo Kohlberger

IPC: G06K9/00 ,  G06K9/62 ,  G16H30/40 ,  G06N20/00 ,  G06K9/03 ,  G06N3/08

Abstract: 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.