顕微鏡スライド画像のための焦点重み付き機械学習分類器誤り予測

Japanese Patent JP7134303

B2

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.

Martin stamp
Timo Cole Burger

JP2021099377A

September 09, 2022

June 15, 2021

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Google LLC

G01N33/483; G01N33/48; G06T7/00; G16H30/40

JP2020535502A
JP2020531971A
JP2014521926A

WO2018031674A1

Jinsun Park et al.,A Unified Approach of Multi-scale Deep and Hand-Crafted Features for Defocus Estimation,2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR),米国,IEEE,2017年04月28日,pp.2760 - 2769,https://ieeexplore.ieee.org/document/8099778
山口 雅浩 ほか,定量的病理診断を可能とする病理画像認識技術の開発,電子情報通信学会技術研究報告,日本,社団法人電子情報通信学会,2012年01月12日,第111巻, 第389号,165 - 170頁

Murayama Yasuhiko
Shinya Mihiro
Tatsuhiko Abe

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