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The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Patent No.:

US 11657099 B1

PDFFull Text
Date of Patent:
May. 23, 2023

Filed:

Jul. 14, 2020

Information processing apparatus evaluating similarity between medical data, information processing method, and storage medium

Applicant:

Canon Kabushiki Kaisha, Tokyo, JP;

Inventor:

Toru Kikuchi, Hino, JP;

Assignee:

CANON KABUSHIKI KAISHA, Tokyo, JP;

Attorney:

Canon U.S.A., Inc. IP Division

Primary Examiner:

Linh Giang Le

Assistant Examiner:

Constantine Siozopoulos

Int. Cl.
CPC ...
G16H 50/20 (2018.01); G16H 50/70 (2018.01); G06F 16/906 (2019.01); G06F 16/28 (2019.01); G16H 70/20 (2018.01); G16H 30/40 (2018.01); A61B 6/03 (2006.01); G06N 5/048 (2023.01); A61B 6/00 (2006.01); G16H 30/20 (2018.01); G06F 18/214 (2023.01); G06F 18/21 (2023.01); G06V 10/25 (2022.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01);
U.S. Cl.
CPC ...
G06F 16/906 (2019.01); A61B 6/032 (2013.01); A61B 6/5217 (2013.01); G06F 16/285 (2019.01); G06F 18/214 (2023.01); G06F 18/2193 (2023.01); G06N 5/048 (2013.01); G06V 10/25 (2022.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G16H 50/70 (2018.01); G16H 70/20 (2018.01);
Abstract

An information processing apparatus according to an exemplary embodiment includes a likelihood acquisition unit, a first evaluation unit, a determination unit, and a second evaluation unit. The likelihood acquisition unit is configured to acquire class likelihood for first medical data given a correct answer label, by using a first classifier for classifying medical data. The first evaluation unit is configured to evaluates a degree of deviance based on the class likelihood acquired by the likelihood acquisition unit and a class answer corresponding to the correct answer label. The determination unit is configured to determine whether the degree of deviance by the first evaluation unit satisfies a predetermined criterion. The second evaluation unit is configured to, based on second medical data satisfying the predetermined criterion, out of the first medical data, evaluate a similarity between the second medical data and third medical data to be subjected to classification.

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