Xinchen Ye
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Pulmonary Textures Classification

Pulmonary Textures Classification Using A Deep Neural Network with Appearence and Geometry Cues


Classification of pulmonary textures on CT images is essential for the development of a computer-aided diagnosis system of diffuse lung diseases. In this paper, we propose a novel method to classify pulmonary textures by using a deep neural network, which can make full use of appearance and geometry cues of textures via a dual-branch architecture. The proposed method has been evaluated by a dataset that includes seven kinds of typical pulmonary textures. Experimental results show that our method outperforms the state-of-the-art methods including feature engineering based method and convolutional neural network based method.

Index Terms— residual network, pulmonary texture, Hessian matrix, CAD, CT



Opening soon. The source code is only for the non-commercial use.


[1]   Rui Xu, Zhen Cong, Xinchen Ye*, Pulmonary Textures Classification Using A Deep Neural Network with Appearence and Geometry Cues, IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2018, Calgary, Alberta, Canada.CCF-B

[2]  Rui Xu, Jiao Pan, Xinchen Ye, S. Kido and S. Tanaka, "A pilot study to utilize a deep convolutional network to segment lungs with complex opacities," IEEE Chinese Automation Congress (CAC), Jinan, 2017, pp. 3291-3295.

Personal information

Associate Professor
Supervisor of Master's Candidates

Main positions:IEEE member, ACM member

Other Post:None


Alma Mater:Dalian University of Technology

Degree:Doctoral Degree

School/Department:School of Software Technology

Discipline:Software Engineering

Business Address:Teaching Building C507, Campus of Development Zone, Dalian, China.


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Address: No.2 Linggong Road, Ganjingzi District, Dalian City, Liaoning Province, P.R.C., 116024

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