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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 11763931 B1

PDFFull Text
Date of Patent:
Sep. 19, 2023

Filed:

Apr. 08, 2019

Rule out accuracy for detecting findings of interest in images

Applicant:

Merative Us L.p., Ann Arbor, MI (US);

Inventors:

Alexandros Karargyris, San Jose, CA (US);

Chun Lok Wong, San Jose, CA (US);

Joy Wu, San Jose, CA (US);

Mehdi Moradi, San Jose, CA (US);

Assignee:

MERATIVE US L.P., Ann Arbor, MI (US);

Attorney:

Michael Best & Friedrich LLP

Primary Examiner:

Tom Y Lu

Int. Cl.
CPC ...
G06T 7/00 (2017.01); G16H 30/40 (2018.01); G06N 20/00 (2019.01); G16H 15/00 (2018.01); G06F 40/169 (2020.01); G06F 18/24 (2023.01); G06F 18/21 (2023.01); G06F 18/214 (2023.01); G06V 10/774 (2022.01); G06V 10/776 (2022.01); G06F 40/30 (2020.01);
U.S. Cl.
CPC ...
G16H 30/40 (2018.01); G06F 18/217 (2023.01); G06F 18/2148 (2023.01); G06F 18/24 (2023.01); G06F 40/169 (2020.01); G06N 20/00 (2019.01); G06T 7/0012 (2013.01); G06V 10/774 (2022.01); G06V 10/776 (2022.01); G16H 15/00 (2018.01); G06F 40/30 (2020.01); G06T 2207/10116 (2013.01); G06T 2207/20081 (2013.01);
Abstract

Methods and systems are directed to training an artificial intelligence engine. One system includes an electronic processor configured obtain a set of reports corresponding to a set of medical images, determine a label for a finding of interest, and identify one or more ambiguous reports in the set of repots. Ambiguous reports do not include a positive label or a negative label for the finding of interest. The electronic processor is also configured to generate an annotation for each of the one or more ambiguous reports in the set of reports, and train the artificial intelligence engine using a training set including the annotation for each of the one or more ambiguous reports and non-ambiguous reports in the set of reports. A result of the training is generation of a classification model for the label for the finding of interest.

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