Invention Grant
- Patent Title: Focus-weighted, machine learning disease classifier error prediction for microscope slide images
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Application No.: US17493066Application Date: 2021-10-04
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Publication No.: US11657487B2Publication Date: 2023-05-23
- Inventor: Martin Stumpe , Timo Kohlberger
- Applicant: Google LLC
- Applicant Address: US CA Mountain View
- Assignee: Google LLC
- Current Assignee: Google LLC
- Current Assignee Address: US CA Mountain View
- Agency: McDonnell Boehnen Hulbert & Berghoff LLP
- The original application number of the division: US16883014 2020.05.26
- Main IPC: G06K9/00
- IPC: G06K9/00 ; G06T7/00 ; G16H30/40 ; G06N20/00 ; G06N3/08 ; G06V20/69 ; G06F18/21

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.
Public/Granted literature
- US20220027678A1 Focus-Weighted, Machine Learning Disease Classifier Error Prediction for Microscope Slide Images Public/Granted day:2022-01-27
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