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:
Aug. 25, 2026

Filed:

Jun. 29, 2021
Applicant:

The Brigham and Women's Hospital, Inc., Boston, MA (US);

Inventors:

Hadi Shafiee, Boston, MA (US);

Prudhvi Thirumalaraju, Watertown, MA (US);

Manoj Kumar Kanakasabapathy, Boston, MA (US);

Sai Hemanth Kumar Kandula, Watertown, MA (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2022.01); G06T 7/00 (2017.01); G06V 10/762 (2022.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01); G06V 20/69 (2022.01); G16H 50/20 (2018.01);
U.S. Cl.
CPC ...
G06T 7/0016 (2013.01); G06V 10/762 (2022.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01); G06V 20/698 (2022.01); G16H 50/20 (2018.01); G06T 2207/10056 (2013.01); G06T 2207/20021 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30024 (2013.01); G06V 2201/03 (2022.01);
Abstract

Systems and methods are provided for medical image classification of images from varying sources. A set of microscopic medical images are acquired, and a first neural network module configured to reduce each of the set of microscopic medical images to a feature representation is generated. The first neural network module, a second neural network module, and a third neural network module are trained on at least a subset of the set of microscopic medical images. The second neural network module is trained to receive feature representation associated with an image of the microscopic images and classify the image into one of a first plurality of output classes. The third neural network module is trained to receive the feature representation, classify the image into one of a second plurality of output classes based on the feature representation, and provide feedback to the first neural network module.


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