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. 16, 2024

Filed:

Oct. 09, 2020
Applicant:

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

Inventors:

Soumya Ghose, Niskayuna, NY (US);

Dattesh Dayanand Shanbhag, Bagalore, IN;

Chitresh Bhushan, Schenectady, NY (US);

Andre De Almeida Maximo, Rio de Janeiro, BR;

Radhika Madhavan, Niskayuna, NY (US);

Desmond Teck Beng Yeo, Clifton Park, NY (US);

Thomas Kwok-Fah Foo, Clifton Park, NY (US);

Assignee:

GE PRECISION HEALTHCARE LLC, Wauwatosa, WI (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 18/214 (2023.01); G06F 18/211 (2023.01); G06F 18/22 (2023.01); G06F 18/232 (2023.01); G06N 3/08 (2023.01); G16H 30/40 (2018.01);
U.S. Cl.
CPC ...
G06F 18/2148 (2023.01); G06F 18/211 (2023.01); G06F 18/2155 (2023.01); G06F 18/22 (2023.01); G06F 18/232 (2023.01); G06N 3/08 (2013.01); G16H 30/40 (2018.01); G06V 2201/03 (2022.01);
Abstract

A computer-implemented method of automatically labeling medical images is provided. The method includes clustering training images and training labels into clusters, each cluster including a representative template having a representative image and a representative label. The method also includes training a neural network model with a training dataset that includes the training images and the training labels, and target outputs of the neural network model are labels of the medical images. The method further includes generating a suboptimal label corresponding to an unlabeled test image using the trained neural network model, and generating an optimal label corresponding to the unlabeled test image using the suboptimal label and representative templates. In addition, the method includes updating the training dataset using the test image and the optimal label, retraining the neural network model, generating a label of an unlabeled image using the retrained neural network model, and outputting the generated label.


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