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. 04, 2023

Filed:

Mar. 25, 2022
Applicant:

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

Inventors:

Li Yao, San Francisco, CA (US);

Jordan Prosky, San Francisco, CA (US);

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

Kevin Lyman, Fords, NJ (US);

Ben Covington, Berkeley, CA (US);

Anthony Upton, Malvern, AU;

Assignee:

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

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06Q 10/0631 (2023.01); G16H 10/60 (2018.01); G16H 30/40 (2018.01); G16H 15/00 (2018.01); G06T 5/00 (2006.01); G06T 5/50 (2006.01); G06T 7/00 (2017.01); G06T 11/00 (2006.01); G06N 5/04 (2023.01); G16H 30/20 (2018.01); G06N 20/00 (2019.01); G06F 9/54 (2006.01); G06T 7/187 (2017.01); G06T 7/11 (2017.01); G06F 3/0482 (2013.01); G06T 3/40 (2006.01); A61B 5/00 (2006.01); G16H 50/20 (2018.01); G06F 21/62 (2013.01); G06Q 20/14 (2012.01); G16H 40/20 (2018.01); G06F 3/0484 (2022.01); G16H 10/20 (2018.01); G06N 5/045 (2023.01); G06T 7/10 (2017.01); G06T 11/20 (2006.01); G06F 16/245 (2019.01); G06T 7/44 (2017.01); G06N 20/20 (2019.01); H04L 67/12 (2022.01); H04L 67/01 (2022.01); G06V 10/82 (2022.01); G06F 18/40 (2023.01); G06F 18/214 (2023.01); G06F 18/21 (2023.01); G06F 18/2115 (2023.01); G06F 18/2415 (2023.01); G06V 10/25 (2022.01); G06V 30/19 (2022.01); G06V 10/764 (2022.01); G06V 40/16 (2022.01); G06V 10/22 (2022.01); G16H 50/70 (2018.01); G06T 7/70 (2017.01); G16H 50/30 (2018.01); A61B 5/055 (2006.01); A61B 6/03 (2006.01); A61B 8/00 (2006.01); A61B 6/00 (2006.01); G06Q 50/22 (2018.01); G06F 40/295 (2020.01); G06F 18/24 (2023.01); G06F 18/2111 (2023.01); G06V 30/194 (2022.01);
U.S. Cl.
CPC ...
G06Q 10/06315 (2013.01); A61B 5/7264 (2013.01); G06F 3/0482 (2013.01); G06F 3/0484 (2013.01); G06F 9/542 (2013.01); G06F 16/245 (2019.01); G06F 18/214 (2023.01); G06F 18/217 (2023.01); G06F 18/2115 (2023.01); G06F 18/2415 (2023.01); G06F 18/41 (2023.01); G06F 21/6254 (2013.01); G06N 5/04 (2013.01); G06N 5/045 (2013.01); G06N 20/00 (2019.01); G06N 20/20 (2019.01); G06Q 20/14 (2013.01); G06T 3/40 (2013.01); G06T 5/002 (2013.01); G06T 5/008 (2013.01); G06T 5/50 (2013.01); G06T 7/0012 (2013.01); G06T 7/0014 (2013.01); G06T 7/10 (2017.01); G06T 7/11 (2017.01); G06T 7/187 (2017.01); G06T 7/44 (2017.01); G06T 7/97 (2017.01); G06T 11/001 (2013.01); G06T 11/006 (2013.01); G06T 11/206 (2013.01); G06V 10/225 (2022.01); G06V 10/25 (2022.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06V 30/19173 (2022.01); G06V 40/171 (2022.01); G16H 10/20 (2018.01); G16H 10/60 (2018.01); G16H 15/00 (2018.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01); G16H 40/20 (2018.01); G16H 50/20 (2018.01); H04L 67/01 (2022.05); H04L 67/12 (2013.01); A61B 5/055 (2013.01); A61B 6/032 (2013.01); A61B 6/5217 (2013.01); A61B 8/4416 (2013.01); G06F 18/2111 (2023.01); G06F 18/24 (2023.01); G06F 40/295 (2020.01); G06Q 50/22 (2013.01); G06T 7/70 (2017.01); G06T 2200/24 (2013.01); G06T 2207/10048 (2013.01); G06T 2207/10081 (2013.01); G06T 2207/10088 (2013.01); G06T 2207/10116 (2013.01); G06T 2207/10132 (2013.01); G06T 2207/20076 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30004 (2013.01); G06T 2207/30008 (2013.01); G06T 2207/30016 (2013.01); G06T 2207/30061 (2013.01); G06V 30/194 (2022.01); G06V 2201/03 (2022.01); G16H 50/30 (2018.01); G16H 50/70 (2018.01);
Abstract

A method includes generating first contrast significance data for a first computer vision model generated from a first training set of medical scans. First significant contrast parameters are identified based on the first contrast significance data. A first re-contrasted training set is generated based on performing a first intensity transformation function on the first training set of medical scans, where the first intensity transformation function utilizes the first significant contrast parameters. A first re-trained model is generated from the first re-contrasted training set, which is associated with corresponding output labels based on abnormality data for the first training set of medical scans. Re-contrasted image data of a new medical scan is generated based on performing the first intensity transformation function. Inference data indicating at least one abnormality detected in the new medical scan is generated based on utilizing the first re-trained model on the re-contrasted image data.


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