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. 15, 2024

Filed:

Apr. 27, 2018
Applicant:

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

Inventors:

Jianming Liang, Scottsdale, AZ (US);

Zongwei Zhou, Tempe, AZ (US);

Jae Shin, Phoenix, AZ (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06N 3/08 (2023.01); G06F 18/21 (2023.01); G06F 18/214 (2023.01); G06F 18/2413 (2023.01); G06F 18/28 (2023.01); G06N 3/045 (2023.01); G06N 3/047 (2023.01); G06V 10/44 (2022.01); G06V 10/764 (2022.01); G06V 10/772 (2022.01); G06V 10/774 (2022.01); G06V 10/776 (2022.01); G06V 10/82 (2022.01);
U.S. Cl.
CPC ...
G06N 3/08 (2013.01); G06F 18/2148 (2023.01); G06F 18/217 (2023.01); G06F 18/2413 (2023.01); G06F 18/28 (2023.01); G06N 3/045 (2023.01); G06N 3/047 (2023.01); G06V 10/454 (2022.01); G06V 10/764 (2022.01); G06V 10/772 (2022.01); G06V 10/7747 (2022.01); G06V 10/776 (2022.01); G06V 10/82 (2022.01);
Abstract

Systems for selecting candidates for labelling and use in training a convolutional neural network (CNN) are provided, the systems comprising: a memory device; and at least one hardware processor configured to: receive a plurality of input candidates, wherein each candidate includes a plurality of identically labelled patches; and for each of the plurality of candidates: determine a plurality of probabilities, each of the plurality of probabilities being a probability that a unique patch of the plurality of identically labelled patches of the candidate corresponds to a label using a pre-trained CNN; identify a subset of candidates of the plurality of input candidates, wherein the subset does not include all of the plurality of candidates, based on the determined probabilities; query an external source to label the subset of candidates to produce labelled candidates; and train the pre-trained CNN using the labelled candidates.


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