Automated pulmonary nodule detection in CT images using 3D deep squeeze-and-excitation networks
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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).
The proposed approach employs 3D DCNNs based on squeeze-and-excitation network and residual network (SE-ResNet) for pulmonary nodule candidate detection and false-positive reduction. Specifically, a 3D region proposal network with a U-Net-like structure is designed for detecting pulmonary nodule candidates. For the subsequent false-positive reduction, a 3D SE-ResNet-based classifier is presented to accurately discriminate the true nodules from candidates. The 3D SE-ResNet modules boost the representational power of the network by adaptively recalibrating channel-wise residual feature responses. Both models utilize 3D SE-ResNet modules to learn nodule features effectively and improve nodule detection performance.
On the public available lung nodule analysis 2016 dataset with 888 scans included, the proposed method reaches high detection sensitivities of 93.6% and 95.7% at one and four false positives per scan, respectively. Meanwhile, the competition performance metric score of 0.904 is achieved. The proposed method has the capability to detect multi-size nodules, especially the extremely small nodules.
In this paper, a 3D DCNNs framework based on 3D SE-ResNet modules is proposed to detect pulmonary nodules in chest CT images accurately. Experimental results demonstrate superior effectiveness of the proposed approach in pulmonary nodule detection task.
KeywordsPulmonary nodule Computer-aided detection Deep learning 3D squeeze-and-excitation network
This research was supported by the National Natural Science Foundation of China (Grant No. 81871457), the National Natural Science Foundation of China (Grant No. 51775368), the National Natural Science Foundation of China (Grant No. 51811530310) and the Science and Technology Project of Tianjin (Grant No. 18YFZCSY01300). We are grateful to the LUNA16 challenge organizers for their efforts in collecting and sharing chest CT scan data for comparing pulmonary nodule detection algorithms.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Informed consent was obtained from all individual participants included in the study.
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