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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.

Patent No.:

US 11756318 B1

PDFFull Text
Date of Patent:
Sep. 12, 2023

Filed:

Feb. 03, 2023

Convolutional neural networks for locating objects of interest in images of biological samples

Applicant:

Ventana Medical Systems, Inc., Tucson, AZ (US);

Inventors:

Srinivas Chukka, San Jose, CA (US);

Jianxu Chen, Granger, IN (US);

Assignee:

VENTANA MEDICAL SYSTEMS, INC., Tucson, AZ (US);

Attorney:

Kilpatrick Townsend & Stockton LLP

Primary Examiner:

Michael S Osinski

Int. Cl.
CPC ...
G06V 20/69 (2022.01); G06V 10/44 (2022.01); G06F 18/214 (2023.01); G06F 18/2413 (2023.01); G06N 3/045 (2023.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01);
U.S. Cl.
CPC ...
G06V 20/69 (2022.01); G06F 18/214 (2023.01); G06F 18/24143 (2023.01); G06N 3/045 (2023.01); G06V 10/454 (2022.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06V 20/698 (2022.01);
Abstract

Convolutional neural networks for detecting objects of interest within images of biological specimens are disclosed. Also disclosed are systems and methods of training and using such networks, one method including: obtaining a sample image and at least one of a set of positive points and a set of negative points, wherein each positive point identifies a location of one object of interest within the sample image, and each negative point identifies a location of one object of no-interest within the sample image; obtaining one or more predefined characteristics of objects of interest and/or objects of no-interest, and based on the predefined characteristics, generating a boundary map comprising a positive area around each positive point the set of positive points, and/or a negative area around each negative point in the set of negative points; and training the convolutional neural network using the sample image and the boundary map.

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