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:
Oct. 20, 2026

Filed:

Apr. 20, 2023
Applicant:

Bristol-myers Squibb Company, Princeton, NJ (US);

Inventors:

Zekai Chen, Gaithersburg, MD (US);

Kevin Alex Brown, Drexel Hill, PA (US);

Assignee:

Bristol-Myers Squibb Company, Princeton, NJ (US);

Attorneys:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06V 10/774 (2022.01); G06N 3/0455 (2023.01); G06N 3/0895 (2023.01); G06T 7/00 (2017.01); G06T 7/11 (2017.01); G06T 9/00 (2006.01); G06V 10/26 (2022.01); G06V 10/776 (2022.01); G16H 30/20 (2018.01);
U.S. Cl.
CPC ...
G06V 10/7753 (2022.01); G06N 3/0455 (2023.01); G06N 3/0895 (2023.01); G06T 7/0012 (2013.01); G06T 7/11 (2017.01); G06T 9/002 (2013.01); G06V 10/26 (2022.01); G06V 10/776 (2022.01); G06T 2200/04 (2013.01); G06T 2207/10081 (2013.01); G06T 2207/10088 (2013.01); G06T 2207/20021 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30096 (2013.01); G06V 2201/03 (2022.01); G16H 30/20 (2018.01);
Abstract

A method includes obtaining a first training data set including unannotated multi-dimensional medical images and executing a self-supervised masked image modeling (MIM) training process to pre-train an image encoder on the first training data set. The method also includes obtaining a second training data set that includes annotated multi-dimensional medical images. Here, each annotated multi-dimensional medical image includes a plurality of image voxels each paired with a corresponding ground-truth label indicating a class the corresponding image voxel belongs to. The method also includes executing a supervised training process to train an image analysis model on the second training data set to teach the image analysis model to learn how to predict the corresponding ground-truth labels for the plurality of image voxels of each annotated multi-dimensional medical image. The image analysis model incorporates the pre-trained image encoder.


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Chen, Zekai. (2026). Medical imaging analysis using self-supervised learning (U.S. Patent No. 12770378). U.S. Patent and Trademark Office. https://idiyas.com/patent/badge/12770378

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