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:
Jun. 04, 2024

Filed:

Dec. 23, 2022
Applicant:

Nantomics, Llc, Culver City, CA (US);

Inventors:

Bing Song, La Canada, CA (US);

Gregory Chu, Los Angeles, CA (US);

Assignee:

NantOmics, LLC, Culver City, CA (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06V 10/82 (2022.01); G06F 18/20 (2023.01); G06F 18/21 (2023.01); G06F 18/23213 (2023.01); G06F 18/2411 (2023.01); G06F 18/2413 (2023.01); G06F 18/2415 (2023.01); G06N 3/04 (2023.01); G06N 3/045 (2023.01); G06N 7/01 (2023.01); G06T 7/00 (2017.01); G06T 7/11 (2017.01); G06T 7/187 (2017.01); G06V 10/44 (2022.01); G06V 10/50 (2022.01); G06V 10/764 (2022.01); G06V 10/778 (2022.01); G06V 20/69 (2022.01); G06N 20/00 (2019.01);
U.S. Cl.
CPC ...
G06V 10/82 (2022.01); G06F 18/21 (2023.01); G06F 18/2193 (2023.01); G06F 18/23213 (2023.01); G06F 18/2411 (2023.01); G06F 18/24137 (2023.01); G06F 18/24147 (2023.01); G06F 18/24155 (2023.01); G06F 18/285 (2023.01); G06F 18/29 (2023.01); G06N 3/04 (2013.01); G06N 3/045 (2023.01); G06N 7/01 (2023.01); G06T 7/0012 (2013.01); G06T 7/11 (2017.01); G06T 7/187 (2017.01); G06V 10/454 (2022.01); G06V 10/50 (2022.01); G06V 10/764 (2022.01); G06V 10/7796 (2022.01); G06V 20/695 (2022.01); G06N 20/00 (2019.01); G06T 2207/10056 (2013.01); G06T 2207/20021 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/20156 (2013.01); G06T 2207/30024 (2013.01);
Abstract

A computer implemented method of generating at least one shape of a region of interest in a digital image is provided. The method includes obtaining, by an image processing engine, access to a digital tissue image of a biological sample; tiling, by the image processing engine, the digital tissue image into a collection of image patches; identifying, by the image processing engine, a set of target tissue patches from the collection of image patches as a function of pixel content within the collection of image patches; assigning, by the image processing engine, each target tissue patch of the set of target tissue patches an initial class probability score indicating a probability that the target tissue patch falls within a class of interest, the initial class probability score generated by a trained classifier executed on each target tissue patch; generating, by the image processing engine, a first set of tissue region seed patches by identifying target tissue patches having initial class probability scores that satisfy a first seed region criteria, the first set of tissue region seed patches comprising a subset of the set of target tissue patches; generating, by the image processing engine, a second set of tissue region seed patches by identifying target tissue patches having initial class probability scores that satisfy a second seed region criteria, the second set of tissue region seed patches comprising a subset of the set of target tissue patches; calculating, by the image processing engine, a region of interest score for each patch in the second set of tissue region seed patches as a function of initial class probability scores of neighboring patches of the second set of tissue region seed patches and a distance to patches within the first set of issue region seed patches; and generating, by the image processing engine, one or more region of interest shapes by grouping neighboring patches based on their region of interest scores.


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