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.

Date of Patent:
Mar. 05, 2024

Filed:

Apr. 02, 2021
Applicant:

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

Inventors:

Mehdi Moradi, San Jose, CA (US);

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

Assignee:

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

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06V 10/82 (2022.01); G06F 18/24 (2023.01); G06N 3/044 (2023.01); G06N 3/045 (2023.01); G06N 3/047 (2023.01); G06N 3/08 (2023.01); G06N 5/01 (2023.01); G06N 20/00 (2019.01); G06N 20/10 (2019.01); G06N 20/20 (2019.01); G06T 7/00 (2017.01); G06V 10/764 (2022.01); G06V 20/69 (2022.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G16H 50/70 (2018.01);
U.S. Cl.
CPC ...
G06V 10/82 (2022.01); G06F 18/24 (2023.01); G06N 3/045 (2023.01); G06N 3/08 (2013.01); G06N 20/00 (2019.01); G06T 7/0012 (2013.01); G06T 7/0014 (2013.01); G06V 10/764 (2022.01); G06V 20/69 (2022.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G16H 50/70 (2018.01); G06N 3/044 (2023.01); G06N 3/047 (2023.01); G06N 5/01 (2023.01); G06N 20/10 (2019.01); G06N 20/20 (2019.01); G06T 2207/10081 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30004 (2013.01); G06T 2207/30048 (2013.01); G06V 2201/03 (2022.01);
Abstract

Disease detection from medical images is provided. In various embodiments, a medical image of a patient is read. The medical image is provided to a trained anatomy segmentation network. A feature map is received from the trained anatomy segmentation network. The feature map indicates the location of at least one feature within the medical image. The feature map is provided to a trained classification network. The trained classification network was pre-trained on a plurality of feature map outputs of the segmentation network. A disease detection is received from the trained classification network. The disease detection indicating the presence or absence of a predetermined disease.


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