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:
May. 16, 2023

Filed:

Oct. 28, 2020
Applicant:

The Regents of the University of Michigan, Ann Arbor, MI (US);

Inventors:

Kevin Ward, Glen Allen, VA (US);

Daniel Francis Taylor, Belleville, MI (US);

Michael W. Sjoding, Ann Arbor, MI (US);

Christopher Elliot Gillies, Ann Arbor, MI (US);

Assignee:
Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/00 (2017.01); G16H 10/60 (2018.01); G06T 11/20 (2006.01); G06F 17/18 (2006.01);
U.S. Cl.
CPC ...
G06T 7/0012 (2013.01); G06F 17/18 (2013.01); G06T 11/20 (2013.01); G16H 10/60 (2018.01); G06T 2207/10116 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30061 (2013.01); G06T 2210/12 (2013.01);
Abstract

A computer-implemented method includes preprocessing a variable dimension medical image, identifying one or more areas of interest in the medical image; and analyzing the one or more areas of interest using a deep learning model. A computing system includes one or more processors; and one or more memories storing instructions that, when executed by the one or more processors, cause the computing system to preprocess a variable dimension medical image, identify one or more areas of interest in the medical image; and analyze the one or more areas of interest using a deep learning model. A non-transitory computer readable medium contains program instructions that when executed, cause a computer to preprocess a variable dimension medical image, identify one or more areas of interest in the medical image, and analyze the one or more areas of interest using a deep learning model.


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