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Accurate detection of pulmonary nodules on chest computed tomography scans is crucial to early diagnosis of lung cancer. To address the thorn problems on low detection sensitivity and high falsepositive rate caused by heterogeneity and morphological complexity of 3-D nodule features, a computeraided detection system is developed to increase the detection sensitivity and classification accuracy of pulmonary nodules. The contributions include: (1) Nodule candidate detection: 3-D Residual U-Net model is improved to detect candidate nodules, which constructs 3-D context-guided module to extract local and global nodule features by setting the dilated convolution with different dilation rates. Furthermore, channel attention mechanism is used to dynamically adjust the channel features, which enhances the generalization and expression ability of the detection-network to effectively learn 3-D spatial context features. (2) False-positive reduction: multi-branch classification network is designed for multi-task learning. Image reconstruction task is performed to retain more microscopic nodules information from convolutional neural network (CNN) hierarchy. Moreover, each branch deals with the feature map at corresponding depth layers, and then all branches' feature maps are combined together to perform nodule classification task. Numerous experimental results show that the proposed system is perfectly qualified for pulmonary nodules detection on Lung Nodules Analysis 2016 dataset, which achieves detection sensitivity up to 94.0% and competition performance metric (CPM) score up to 0.959.

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