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. 18, 2024

Filed:

Jan. 04, 2021
Applicants:

Case Western Reserve University, Cleveland, OH (US);

The United States Government As Represented BY the Department of Veteran Affairs, Washington, DC (US);

Uh Cleveland Medical Center, Cleveland, OH (US);

Inventors:

Anant Madabhushi, Shaker Heights, OH (US);

Neeraj Kumar, Cleveland Heights, OH (US);

Joseph E. Willis, Shaker Heights, OH (US);

Assignees:

Case Western Reserve University, Cleveland, OH (US);

UH Cleveland Medical Center, Cleveland, OH (US);

The United States Government as Represente, Washington, DC (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06T 7/00 (2017.01); G06T 7/11 (2017.01); G06T 7/41 (2017.01); G06T 7/62 (2017.01); G16H 10/40 (2018.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G16H 70/60 (2018.01);
U.S. Cl.
CPC ...
G06T 7/0012 (2013.01); G06T 7/11 (2017.01); G06T 7/41 (2017.01); G06T 7/62 (2017.01); G16H 10/40 (2018.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G16H 70/60 (2018.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30024 (2013.01); G06T 2207/30028 (2013.01); G06T 2207/30081 (2013.01); G06T 2207/30096 (2013.01);
Abstract

Embodiments discussed herein facilitate determination of cancer stages based at least in part on shape, size, and/or texture features of cancer nuclei. One example embodiment is a method, comprising: accessing at least a portion of a digital whole slide image (WSI) comprising a tumor; segmenting (e.g., via a first deep learning model) the tumor on the at least the portion of the digital WSI; segmenting (e.g., via a second deep learning model) cancer nuclei in the segmented tumor; extracting one or more features from the segmented cancer nuclei; providing the one or more features extracted from the segmented cancer nuclei to a trained machine learning model; and receiving, from the machine learning model, an indication of a cancer stage of the tumor.


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