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:
Jul. 16, 2024

Filed:

Jun. 15, 2021
Applicants:

Sony Group Corporation, Tokyo, JP;

Sony Corporation of America, New York, NY (US);

Inventors:

Bi Song, San Jose, CA (US);

Ko-Kai Albert Huang, Cupertino, CA (US);

Ming-Chang Liu, San Jose, CA (US);

Assignees:

SONY GROUP CORPORATION, Tokyo, JP;

SONY CORPORATION OF AMERICA, New York, NY (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/00 (2017.01); G06N 3/045 (2023.01); G06N 3/08 (2023.01); G16H 10/40 (2018.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G16H 70/60 (2018.01);
U.S. Cl.
CPC ...
G06T 7/0012 (2013.01); G06N 3/045 (2023.01); G06N 3/08 (2013.01); G16H 10/40 (2018.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G16H 70/60 (2018.01); G06T 2207/10056 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30024 (2013.01); G06T 2207/30096 (2013.01);
Abstract

A method for automatically estimating cellularity in a digital pathology slide image includes: extracting patches of interest from the digital pathology slide image; operating on each patch using a trained first deep convolutional neural network (DCNN) to classify that patch as either normal, having an estimated cellularity of 0%, or suspect, having a cellularity roughly estimated to be greater than 0%; operating on each suspect patch using a second DCNN, trained using a deep ordinal regression model, to determine an estimated cellularity score for that suspect patch; and combining the estimated cellularity scores of the patches of interest to provide an estimated cellularity for the digital pathology slide image at a patch-by-patch level.


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