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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.

Patent No.:

US 12444054 B1

PDFFull Text
Date of Patent:
Oct. 14, 2025

Filed:

Feb. 20, 2025

Localization and classification of abnormalities in medical images

Applicant:

Siemens Healthineers Ag, Forchheim, DE;

Inventors:

Ali Kamen, Skillman, NJ (US);

Bin Lou, Princeton Junction, NJ (US);

Bibo Shi, Monmouth Junction, NJ (US);

Nicolas Von Roden, St Gallen, CH;

Berthold Kiefer, Erlangen, DE;

Robert Grimm, Nuremberg, DE;

Heinrich Von Busch, Uttenreuth, DE;

Mamadou Diallo, Plainsboro, NJ (US);

Tongbai Meng, Ellicott City, MD (US);

Dorin Comaniciu, Princeton, NJ (US);

David Jean Winkel, Basel, CH;

Xin Yu, Nashville, TN (US);

Assignee:

Siemens Healthineers AG, Forchheim, DE;

Attorney:
Primary Examiner:

Van D Huynh

Int. Cl.
CPC ...
G06T 7/00 (2017.01); G06F 18/214 (2023.01); G06F 18/2411 (2023.01); G06N 20/00 (2019.01); G06T 7/11 (2017.01); G06V 10/26 (2022.01); G06V 10/44 (2022.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01);
U.S. Cl.
CPC ...
G06T 7/0014 (2013.01); G06F 18/214 (2023.01); G06F 18/2411 (2023.01); G06N 20/00 (2019.01); G06T 7/0012 (2013.01); G06T 7/11 (2017.01); G06V 10/26 (2022.01); G06V 10/454 (2022.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06T 2207/10088 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30096 (2013.01);
Abstract

Systems and methods are provided for optimizing a deep learning model. A multi-site dataset associated with different clinical sites and a deployment dataset associated with a deployment clinical site are received. A deep learning model is trained based on the multi-site dataset. The trained deep learning model is optimized based on the deployment dataset. The optimized trained deep learning model is output.

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