Automated pulmonary nodule detection in CT images using 3D deep squeeze-and-excitation networks.

Journal: International journal of computer assisted radiology and surgery

Published Date: Apr 26, 2019

Abstract

PURPOSE: Pulmonary nodule detection has great significance for early treating lung cancer and increasing patient survival. This work presents a novel automated computer-aided detection scheme for pulmonary nodules based on deep convolutional neural networks (DCNNs).

Authors

Li Gong

School of Mechanical Engineering, Tianjin University, 135 Yaguan Road, Jinnan District, Tianjin, 300350, China.
Shan Jiang

Emergency Center, Hubei Clinical Research Center for Emergency and Resuscitaion, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China.
Zhiyong Yang
Guobin Zhang

School of Mechanical Engineering, Tianjin University, 135 Yaguan Road, Jinnan District, Tianjin, 300350, China.
Lu Wang

Department of Laboratory, Akesu Center of Disease Control and Prevention, Akesu, China.

Keywords

Humans Imaging, Three-Dimensional Lung Lung Neoplasms Neural Networks, Computer Solitary Pulmonary Nodule Tomography, X-Ray Computed

External Resources

View on PubMed Access via DOI PubMed (31028657)

Automated pulmonary nodule detection in CT images using 3D deep squeeze-and-excitation networks.

Abstract

Authors

Keywords

External Resources

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