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:
Nov. 09, 2021

Filed:

Jan. 07, 2019
Applicant:

Dalian University of Technology, Dalian, CN;

Inventors:

Rui Xu, Dalian, CN;

Xinchen Ye, Dalian, CN;

Lin Lin, Dalian, CN;

Haojie Li, Dalian, CN;

Xin Fan, Dalian, CN;

Zhongxuan Luo, Dalian, CN;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/41 (2017.01); G06N 3/08 (2006.01); G06T 7/00 (2017.01); G06N 3/04 (2006.01);
U.S. Cl.
CPC ...
G06T 7/0012 (2013.01); G06N 3/04 (2013.01); G06N 3/08 (2013.01); G06T 7/41 (2017.01); G06T 2207/10081 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30061 (2013.01);
Abstract

Provided is a method based on deep neural network to extract appearance and geometry features for pulmonary textures classification, which belongs to the technical fields of medical image processing and computer vision. Taking 217 pulmonary computed tomography images as original data, several groups of datasets are generated through a preprocessing procedure. Each group includes a CT image patch, a corresponding image patch containing geometry information and a ground-truth label. A dual-branch residual network is constructed, including two branches separately takes CT image patches and corresponding image patches containing geometry information as input. Appearance and geometry information of pulmonary textures are learnt by the dual-branch residual network, and then they are fused to achieve high accuracy for pulmonary texture classification. Besides, the proposed network architecture is clear, easy to be constructed and implemented.


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