Method based on deep neural network to extract appearance and geometry features for pulmonary textures classification

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United States of America Patent

PATENT NO 11170502
APP PUB NO 20200258218A1
SERIAL NO

16649650

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Abstract

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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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Patent Owner(s)

  • DALIAN UNIVERSITY OF TECHNOLOGY

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Inventor(s)

Inventor Name Address # of filed Patents Total Citations
Fan, Xin Dalian, CN 62 685
Li, Haojie Dalian, CN 21 22
Lin, Lin Dalian, CN 230 1523
Luo, Zhongxuan Dalian, CN 29 16
Xu, Rui Dalian, CN 388 1800
Ye, Xinchen Dalian, CN 7 11

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