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:
Oct. 29, 2024

Filed:

Jan. 28, 2020
Applicants:

Dignity Health, San Francisco, CA (US);

Arizona Board of Regents on Behalf of Arizona State University, Scottsdale, AZ (US);

Inventors:

Mohammadhassan Izadyyazdanabadi, Tempe, AZ (US);

Mark C. Preul, Scottsdale, AZ (US);

Evgenii Belykh, Phoenix, AZ (US);

Yezhou Yang, Phoenix, AZ (US);

Assignees:
Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06T 7/00 (2017.01); A61B 1/00 (2006.01); A61B 1/06 (2006.01); A61B 1/313 (2006.01); A61B 90/20 (2016.01); G06N 3/045 (2023.01);
U.S. Cl.
CPC ...
G06T 7/0012 (2013.01); A61B 1/0005 (2013.01); A61B 1/063 (2013.01); A61B 1/313 (2013.01); A61B 90/20 (2016.02); G06N 3/045 (2023.01); G06T 2207/10056 (2013.01); G06T 2207/10068 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30016 (2013.01); G06T 2207/30096 (2013.01);
Abstract

In accordance with some embodiments of the disclosed subject matter, systems, methods, and media for automatically transforming a digital image into a simulated pathology image are provided. In some embodiments, the method comprises: receiving a content image from an endomicroscopy device; receiving, from a hidden layer of a convolutional neural network (CNN) trained to recognize a multitude of classes of common objects, features indicative of content of the content image; receiving, providing a style reference image to the CNN; receiving, from another hidden layer of the CNN, features indicative of a style of the style reference image; receiving, from the hidden layers of the CNN, features indicative of content and style of a target image; generating a loss value based on the features of the content image, the style reference image, and the target image; minimizing the loss value; and displaying the target image with the minimized loss.


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