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:
Sep. 05, 2023

Filed:

Aug. 04, 2021
Applicant:

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

Inventors:

Jordan Prosky, San Francisco, CA (US);

Li Yao, 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:
Int. Cl.
CPC ...
G06T 5/50 (2006.01); G06F 9/54 (2006.01); G06N 5/04 (2023.01); 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 7/00 (2017.01); G06T 11/00 (2006.01); G16H 30/20 (2018.01); G06N 20/00 (2019.01); 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 multi-model medical scan analysis system is operable to generate a generic model by performing a training step on image data of a plurality of medical scans and corresponding labeling data. A plurality of fine-tuned models are generated by performing a fine-tuning step on the generic model. Abnormality detection data is generated for a new medical scan by utilizing the generic model. A first one of the plurality of abnormality types that is detected in the new medical scan is determined based on a corresponding one of the plurality of probability values. Additional abnormality data is generated by performing a fine-tuned inference function on the image data of the new medical scan that utilizes one of the plurality of fine-tuned models that corresponds to the first one of the plurality of abnormality types. The additional abnormality data is transmitted for display.


Find Patent Forward Citations

Loading…