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:
Feb. 25, 2025

Filed:

Jul. 03, 2023
Applicant:

Paige.ai, Inc., New York, NY (US);

Inventors:

Brandon Rothrock, Los Angeles, CA (US);

Jillian Sue, New York, NY (US);

Matthew Houliston, Boston, MA (US);

Patricia Raciti, New York, NY (US);

Leo Grady, Darien, CT (US);

Assignee:

Paige.AI, Inc., New York, NY (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06V 20/69 (2022.01); G06F 18/2431 (2023.01); G06N 20/00 (2019.01); G06T 7/00 (2017.01); G06T 7/11 (2017.01); G06T 7/194 (2017.01); G16H 30/40 (2018.01);
U.S. Cl.
CPC ...
G06V 20/695 (2022.01); G06F 18/2431 (2023.01); G06N 20/00 (2019.01); G06T 7/0012 (2013.01); G06T 7/11 (2017.01); G06T 7/194 (2017.01); G06V 20/698 (2022.01); G16H 30/40 (2018.01); G06T 2207/20081 (2013.01); G06T 2207/30024 (2013.01);
Abstract

A method of using machine learning to output task-specific predictions may include receiving a digitized cytology image of a cytology sample and applying a machine learning model to isolate cells of the digitized cytology image. The machine learning model may include identifying a plurality of sub-portions of the digitized cytology image, identifying, for each sub-portion of the plurality of sub-portions, either background or cell, and determining cell sub-images of the digitized cytology image. Each cell sub-image may comprise a cell of the digitized cytology image, based on the identifying either background or cell. The method may further comprise determining a plurality of features based on the cell sub-images, each of the cell sub-images being associated with at least one of the plurality of features, determining an aggregated feature based on the plurality of features, and training a machine learning model to predict a target task based on the aggregated feature.


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