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. 26, 2026

Filed:

Sep. 21, 2023
Applicant:

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

Inventors:

Hariharan Ravishankar, Bengaluru, IN;

Vikram Reddy Melapudi, Bangalore, IN;

Pavan Annangi, Bangalore, IN;

Abhijit Patil, Bengaluru, IN;

Assignee:

GE Precision Healthcare LLC, Waukesha, WI (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06V 20/70 (2022.01); G06N 5/04 (2023.01); G06V 10/774 (2022.01); G06V 10/776 (2022.01); G16H 30/40 (2018.01); G16H 10/60 (2018.01);
U.S. Cl.
CPC ...
G06V 20/70 (2022.01); G06N 5/04 (2013.01); G06V 10/774 (2022.01); G06V 10/776 (2022.01); G16H 30/40 (2018.01); G06V 2201/03 (2022.01); G16H 10/60 (2018.01);
Abstract

An iterative framework for learning multimodal mappings tailored to medical image inferencing tasks is provided. In an example, a computer-implemented method can comprise receiving multimodal annotation data for medical images, the multimodal annotation data comprising non-image annotation data and image annotation data, and employing one or more machine learning (ML) processes to learn bi-directional mappings between non-image features included in the non-image annotation data and image features associated with the medical images and the image annotation data. The method further comprises generating, as a result of the one or more ML processes, a model configured to: infer one or more of the non-image features associated with new medical images given the new medical images, and/or infer one or more of the image features associated with the new medical images given the new medical images and non-image input corresponding to at least some of the non-image annotation data.


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