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:
May. 18, 2021

Filed:

Jun. 04, 2019
Applicant:

University of Central Florida Research Foundation, Inc., Orlando, FL (US);

Inventors:

Ulas Bagci, Orlando, FL (US);

Rodney LaLonde, Orlando, FL (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/34 (2006.01); G06T 7/11 (2017.01); G06T 7/00 (2017.01); G06N 20/10 (2019.01); G06N 3/08 (2006.01);
U.S. Cl.
CPC ...
G06T 7/11 (2017.01); G06N 3/084 (2013.01); G06N 20/10 (2019.01); G06T 7/0012 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01);
Abstract

An improved method of performing object segmentation and classification that reduces the memory required to perform these tasks, while increasing predictive accuracy. The improved method utilizes a capsule network with dynamic routing. Capsule networks allow for the preservation of information about the input by replacing max-pooling layers with convolutional strides and dynamic routing, allowing for the reconstruction of an input image from output capsule vectors. The present invention expands the use of capsule networks to the task of object segmentation and medical image-based cancer diagnosis for the first time in the literature; extends the idea of convolutional capsules with locally-connected routing and propose the concept of deconvolutional capsules; extends the masked reconstruction to reconstruct the positive input class; and proposes a capsule-based pooling operation for diagnosis. The convolutional-deconvolutional capsule network shows strong results for the tasks of object segmentation and classification with substantial decrease in parameter space.


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