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. 24, 2023

Filed:

Sep. 18, 2020
Applicant:

The Board of Trustees of the Leland Stanford Junior University, Stanford, CA (US);

Inventors:

Sharon Zhou, Stanford, CA (US);

Andrew Y. Ng, Stanford, CA (US);

Pranav Rajpurkar, Stanford, CA (US);

Mark Sabini, Stanford, CA (US);

Chris Wang, Stanford, CA (US);

Nguyet Minh Phu, Stanford, CA (US);

Amirhossein Kiani, Stanford, CA (US);

Jeremy Irvin, Stanford, CA (US);

Matthew Lungren, Stanford, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/00 (2017.01); G06F 18/241 (2023.01); G06F 18/214 (2023.01); G06V 10/25 (2022.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01);
U.S. Cl.
CPC ...
G06T 7/0012 (2013.01); G06F 18/214 (2023.01); G06F 18/241 (2023.01); G06V 10/25 (2022.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01); G06T 2207/10016 (2013.01); G06T 2207/10116 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30048 (2013.01);
Abstract

Systems and methods for radiology image classification from noisy images in accordance with embodiments of the invention are illustrated. One embodiment includes noisy image classification device, including a processor, camera circuitry, and a memory containing a noisy image classification application, where the noisy image classification application directs the processor to obtain image data describing a first image taken of a second image using the camera circuitry, where the second image was produced by a medical imaging device, and where the first image is a noisy version of the second image, classify the image data using a neural network trained to be robust to noise, generate an investigation recommendation based on the classification, and provide the investigation recommendation via a display.


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