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. 21, 2026

Filed:

Feb. 28, 2024
Applicant:

Siemens Healthineers Ag, Forchheim, DE;

Inventors:

Han Liu, Nashville, TN (US);

Zhoubing Xu, Princeton, NJ (US);

Sasa Grbic, Plainsboro, NJ (US);

Assignee:

Siemens Healthineers AG, Forchheim, DE;

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G16H 30/40 (2018.01); G06V 10/44 (2022.01); G06V 20/70 (2022.01); G16H 30/20 (2018.01);
U.S. Cl.
CPC ...
G16H 30/40 (2018.01); G06V 10/44 (2022.01); G06V 20/70 (2022.01); G16H 30/20 (2018.01); G06V 2201/07 (2022.01);
Abstract

Systems and methods for performing a medical imaging analysis task are provided. One or more input medical images are received. A medical imaging analysis task is performed based on the one or more input medical images using a machine learning based model. Results of the medical imaging analysis task are output. The machine learning based model is trained by receiving one or more training medical images and ground truth labels identifying one or more anatomical objects in the one or more training medical images, training the machine learning based model for performing the medical imaging analysis task based on the one or more training medical images and the ground truth labels, generating pseudo labels identifying the one or more anatomical objects in the one or more training medical images using the trained machine learning based model, and fine-tuning the trained machine learning based model for performing the medical imaging analysis task based on the one or more training medical images, the ground truth labels, and the generated pseudo labels.


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