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:
Jan. 03, 2023

Filed:

Sep. 26, 2018
Applicant:

Visiongate, Inc., Phoenix, AZ (US);

Inventors:

Michael G. Meyer, Phoenix, AZ (US);

Daniel J. Sussman, Auburn, CA (US);

Rahul Katdare, Bothell, WA (US);

Laimonas Kelbauskas, Chandler, AZ (US);

Alan C. Nelson, Gig Harbor, WA (US);

Randall Mastrangelo, Gaithersburg, MD (US);

Assignee:

VISIONGATE, INC., Woodinville, WA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/00 (2017.01); G16B 40/20 (2019.01); G06T 7/194 (2017.01); G06T 7/11 (2017.01); G01N 15/14 (2006.01); G06K 9/62 (2022.01); G06V 10/40 (2022.01); G06V 20/64 (2022.01); G06V 20/69 (2022.01); G01N 15/10 (2006.01);
U.S. Cl.
CPC ...
G16B 40/20 (2019.02); G01N 15/147 (2013.01); G01N 15/1429 (2013.01); G01N 15/1475 (2013.01); G06K 9/6256 (2013.01); G06T 7/0012 (2013.01); G06T 7/11 (2017.01); G06T 7/194 (2017.01); G06V 10/40 (2022.01); G06V 20/64 (2022.01); G06V 20/695 (2022.01); G06V 20/698 (2022.01); G01N 2015/1006 (2013.01); G01N 2015/1445 (2013.01); G06T 2207/10101 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30024 (2013.01); G06V 2201/03 (2022.01);
Abstract

A classification training method for training classifiers adapted to identify specific mutations associated with different cancer including identifying driver mutations. First cells from mutation cell lines derived from conditions having the number of driver mutations are acquired and 3D image feature data from the number of first cells is identified. 3D cell imaging data from the number of first cells and from other malignant cells is generated, where cell imaging data includes a number of first individual cell images. A second set of 3D cell imaging data is generated from a set of normal cells where the number of driver mutations are expected to occur, where the second set of cell imaging data includes second individual cell images. Supervised learning is conducted based on cell line status as ground truth to generate a classifier.


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