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:
Dec. 05, 2023

Filed:

Mar. 06, 2018
Applicant:

University of Southern California, Los Angeles, CA (US);

Inventors:

David B. Agus, Beverly Hills, CA (US);

Paul Thomas Macklin, Bloomington, IN (US);

Rishi Raghav Rawat, Boulder, CO (US);

Daniel Lee Ruderman, Los Angeles, CA (US);

Assignee:

University of Southern California, Los Angeles, CA (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G01N 33/574 (2006.01); G16H 50/20 (2018.01); G06T 7/00 (2017.01); G06V 20/69 (2022.01); G06V 10/82 (2022.01); G06F 18/211 (2023.01);
U.S. Cl.
CPC ...
G01N 33/574 (2013.01); G06F 18/211 (2023.01); G06T 7/0012 (2013.01); G06V 10/82 (2022.01); G06V 20/695 (2022.01); G06V 20/698 (2022.01); G16H 50/20 (2018.01); G06T 2207/10024 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30024 (2013.01);
Abstract

A method assessing tissue morphology using machine learning includes a step of training a machine learnable device to predict the status of a diagnostic feature in stained tissue samples. The machine learnable device is trained with a characterized set of digital images of stained tissue samples. Each digital image of the characterized set has a known status for the diagnostic feature and an extracted feature map provides values for a extracted feature over an associated 2-dimensional grid of spatial locations. A step of inputting the set of extracted feature maps is inputted into the machine learnable device to form associations therein between the set of extracted feature maps to and the known status for the diagnostic feature to form a trained machine learnable device. The status for the diagnostic feature of a stained tissue sample of unknown status for the diagnostic feature is predicted from the trained machine learnable device.


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