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:
Mar. 09, 2021

Filed:

Aug. 16, 2018
Applicant:

The Regents of the University of California, Oakland, CA (US);

Inventors:

Kyung Hyun Sung, Los Angeles, CA (US);

William Hsu, Westlake Village, CA (US);

Shiwen Shen, Los Angeles, CA (US);

Xinran Zhong, Los Angeles, CA (US);

Assignee:
Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
A61B 5/00 (2006.01); A61B 5/055 (2006.01); G06T 7/00 (2017.01); G06K 9/42 (2006.01); G06K 9/52 (2006.01); G06K 9/62 (2006.01); G06K 9/66 (2006.01); A61B 5/20 (2006.01); A61B 5/08 (2006.01);
U.S. Cl.
CPC ...
A61B 5/7267 (2013.01); A61B 5/055 (2013.01); A61B 5/4381 (2013.01); G06K 9/42 (2013.01); G06K 9/527 (2013.01); G06K 9/6269 (2013.01); G06K 9/66 (2013.01); G06T 7/0012 (2013.01); A61B 5/08 (2013.01); A61B 5/201 (2013.01); A61B 5/4244 (2013.01); G06T 2207/10088 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30096 (2013.01);
Abstract

An automatic classification method for distinguishing between indolent and clinically significant carcinoma using multiparametric MRI (mp-MRI) imaging is provided. By utilizing a convolutional neural network (CNN), which automatically extracts deep features, the hierarchical classification framework avoids deficiencies in current schemes in the art such as the need to provide handcrafted features predefined by a domain expert and the precise delineation of lesion boundaries by a human or computerized algorithm. This hierarchical classification framework is trained using previously acquired mp-MRI data with known cancer classification characteristics and the framework is applied to mp-MRI images of new patients to provide identification and computerized cancer classification results of a suspicious lesion.


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