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:
May. 12, 2026

Filed:

Jun. 05, 2023
Applicants:

GE Precision Healthcare Llc, Wauwatosa, WI (US);

The General Hospital Corporation, Boston, MA (US);

Inventors:

Abder-Rahman Ali, Halifax, CA;

Michael Wang, Wauwatosa, WI (US);

Michael J. Washburn, Brookfield, WI (US);

Yelena Tsymbalenko, Mequon, WI (US);

Anthony E. Samir, Newton, MA (US);

Viksit Kumar, Quincy, MA (US);

Shuhang Wang, Newton, MA (US);

Theodore Pierce, Auburndale, MA (US);

Qian Li, Boston, MA (US);

Arinc Ozturk, Boston, MA (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06V 10/778 (2022.01); A61B 8/00 (2006.01); G06T 7/11 (2017.01);
U.S. Cl.
CPC ...
G06V 10/7792 (2022.01); A61B 8/485 (2013.01); G06T 7/11 (2017.01); G06T 2207/10132 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30056 (2013.01);
Abstract

By example, a method for training a model to segment test images, wherein the test images comprise ultrasound image data, includes: receiving, at a self-supervised learning framework, a first plurality of training images, wherein the first plurality of training images include ultrasound data corresponding to patients' livers; processing the first plurality of plurality of training images with a learning algorithm of the self-supervised learning framework, and responsively adapting a trained model; and receiving, at a supervised learning framework, the trained model and a second plurality of training images, wherein the second plurality of training images include ultrasound data corresponding to patients' livers and annotations of the livers, and responsively adapting the trained model.


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