• Yixin Yang, Xiaoyi Feng, Wenhao Chi, Zhengyang Li, Wenzhe Duan, Haiping Liu, Wenhua Liang, Wei Wang, Ping Chen, Jianxing He, and Bo Liu.
• Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China.
• J Thorac Dis. 2018 Apr 1; 10 (Suppl 7): S867-S875.

AbstractDeep learning techniques have recently emerged as promising decision supporting approaches to automatically analyze medical images for different clinical diagnosing purposes. Diagnosing of pulmonary nodules by using computer-assisted diagnosing has received considerable theoretical, computational, and empirical research work, and considerable methods have been developed for detection and classification of pulmonary nodules on different formats of images including chest radiographs, computed tomography (CT), and positron emission tomography in the past five decades. The recent remarkable and significant progress in deep learning for pulmonary nodules achieved in both academia and the industry has demonstrated that deep learning techniques seem to be promising alternative decision support schemes to effectively tackle the central issues in pulmonary nodules diagnosing, including feature extraction, nodule detection, false-positive reduction, and benign-malignant classification for the huge volume of chest scan data. The main goal of this investigation is to provide a comprehensive state-of-the-art review of the deep learning aided decision support for pulmonary nodules diagnosing. As far as the authors know, this is the first time that a review is devoted exclusively to deep learning techniques for pulmonary nodules diagnosing.

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