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:
Jun. 06, 2023

Filed:

Sep. 03, 2021
Applicant:

Enlitic, Inc., San Francisco, CA (US);

Inventors:

Li Yao, San Francisco, CA (US);

Kevin Lyman, Fords, NJ (US);

Ashwin Jadhav, San Francisco, CA (US);

Eric C. Poblenz, Palo Alto, CA (US);

Anthony Upton, Malvern, AU;

Assignee:

Enlitic, Inc., Fort Collins, CO (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/12 (2017.01); G06T 7/00 (2017.01); G06T 7/187 (2017.01); G06V 10/25 (2022.01); G06F 18/214 (2023.01); G06N 7/01 (2023.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01);
U.S. Cl.
CPC ...
G06T 7/0012 (2013.01); G06F 18/214 (2023.01); G06N 7/01 (2023.01); G06T 7/187 (2017.01); G06V 10/25 (2022.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01);
Abstract

A label generating system operates to generate an artificial intelligence model by: training on a training data set that includes the plurality of medical scans with the corresponding global labels; generating testing global probability data by performing an inference function that utilizes the artificial intelligence model on the plurality of medical scans with the corresponding global labels, wherein the testing global probability data indicates a testing set of global probability values corresponding to the set of abnormality classes, and wherein each of the testing set of global probability values indicates a probability that a corresponding one of the set of abnormality classes is present in each of the plurality of medical scans with the corresponding global labels; comparing the testing set of global probability values to a corresponding confidence threshold for each of the plurality of medical scans selected based on the corresponding one of the global labels; generating an updated training data set by correcting ones of the plurality of medical scans having a corresponding one of the testing set of global probability values that compares unfavorably to the corresponding confidence threshold; and retraining the artificial intelligence model based on the updated training set.


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