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. 02, 2019

Filed:

Aug. 30, 2017
Applicant:

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

Inventors:

Li Yao, San Francisco, CA (US);

Devon Bernard, San Francisco, CA (US);

Kevin Lyman, Fords, NJ (US);

Diogo Almeida, San Francisco, CA (US);

Jeremy Howard, San Francisco, CA (US);

Assignee:

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

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G16H 40/20 (2018.01); G06F 19/00 (2018.01); G06F 3/048 (2013.01); G06T 7/00 (2017.01); G06Q 50/22 (2018.01); G16H 50/20 (2018.01); G16H 10/60 (2018.01); G16H 15/00 (2018.01); G16H 50/30 (2018.01); A61B 6/03 (2006.01); A61B 6/00 (2006.01); G06F 3/16 (2006.01); G06F 17/24 (2006.01); G06F 17/27 (2006.01); G06K 9/62 (2006.01); A61B 5/00 (2006.01); G06N 3/04 (2006.01); G06N 3/08 (2006.01); G06Q 10/10 (2012.01); G06T 7/11 (2017.01); G01T 1/24 (2006.01); H04N 5/32 (2006.01); G16H 40/63 (2018.01); G16H 30/40 (2018.01); G06F 17/22 (2006.01); G06F 17/28 (2006.01); G16H 50/50 (2018.01); H04L 29/08 (2006.01); H04L 29/06 (2006.01); G06K 9/03 (2006.01); G06F 3/0484 (2013.01); G06F 3/0485 (2013.01); G06T 11/00 (2006.01);
U.S. Cl.
CPC ...
G16H 40/20 (2018.01); A61B 5/002 (2013.01); A61B 5/0022 (2013.01); A61B 6/032 (2013.01); A61B 6/4233 (2013.01); A61B 6/463 (2013.01); A61B 6/50 (2013.01); A61B 6/503 (2013.01); A61B 6/5217 (2013.01); A61B 6/5288 (2013.01); A61B 6/5294 (2013.01); A61B 6/563 (2013.01); G01T 1/247 (2013.01); G06F 3/048 (2013.01); G06F 3/167 (2013.01); G06F 17/2288 (2013.01); G06F 17/241 (2013.01); G06F 17/2765 (2013.01); G06F 17/2785 (2013.01); G06F 17/2795 (2013.01); G06F 17/2881 (2013.01); G06F 19/321 (2013.01); G06F 19/328 (2013.01); G06K 9/6215 (2013.01); G06N 3/04 (2013.01); G06N 3/084 (2013.01); G06Q 10/10 (2013.01); G06Q 10/103 (2013.01); G06Q 50/22 (2013.01); G06T 7/0012 (2013.01); G06T 7/0016 (2013.01); G06T 7/11 (2017.01); G16H 10/60 (2018.01); G16H 15/00 (2018.01); G16H 30/40 (2018.01); G16H 40/63 (2018.01); G16H 50/20 (2018.01); G16H 50/30 (2018.01); H04N 5/32 (2013.01); G06F 3/0485 (2013.01); G06F 3/04842 (2013.01); G06F 19/3418 (2013.01); G06K 9/03 (2013.01); G06K 9/6202 (2013.01); G06K 9/6256 (2013.01); G06K 9/6267 (2013.01); G06K 2209/05 (2013.01); G06T 11/003 (2013.01); G06T 2207/10081 (2013.01); G06T 2207/10088 (2013.01); G06T 2207/10104 (2013.01); G06T 2207/10116 (2013.01); G06T 2207/10132 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30004 (2013.01); G06T 2207/30061 (2013.01); G06T 2207/30068 (2013.01); G16H 50/50 (2018.01); H04L 67/12 (2013.01); H04L 67/42 (2013.01);
Abstract

A medical scan image analysis system is operable to receive a plurality of medical scans that represent a three-dimensional anatomical region and include a plurality of cross-sectional image slices. A plurality of three-dimensional subregions corresponding to each of the plurality of medical scans are generated by selecting a proper subset of the plurality of cross-sectional image slices from each medical scan, and by further selecting a two-dimensional subregion from each proper subset of cross-sectional image slices. A learning algorithm is performed on the plurality of three-dimensional subregions to generate a fully convolutional neural network. Inference data corresponding to a new medical scan received via the network is generated by performing an inference algorithm on the new medical scan by utilizing the fully convolutional neural network. An inferred abnormality is identified in the new medical scan based on the inference data.


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