An attentive and adaptive 3D CNN for automatic pulmonary nodule detection in CT image

Automatic pulmonary nodule detection is the most crucial technology in early diagnosis of lung cancer and early treatment. However, the various nodule type, shape and size, especially the diameter of lung nodules (ranging from 3 mm to 30 mm), make the lung nodule detection results with high false positives. It significantly affects the detection performance of lung nodules. In this paper, an adaptive and attentive 3D Convolutional Neural Network (CNN) is proposed for automatic pulmonary nodule detection, which contains two parts: the candidate nodule detection and false positive reduction. In the first stage, the globule, spital and fine-grained information of focal nodules are scratched by the high-resolution fused attention module in the proposed method. In the second stage, an adaptive 3D CNN structure is designed to further reduce the false positives, which extracts the multilevel contextual information via an adaptive 3D convolution kernel. Extensive experiments are conducted on publicly available LUNA16. The results demonstrate that the proposed method can increase the sensitivity and decrease the false positives rate for automated pulmonary nodule detection effectively. •An adaptive and attentive 3D CNN is proposed for pulmonary nodule detection.•High resolution features are fused to improve the performance of nodule detection.•An attentive module is designed to scratch the global and spatial information.•Adaptive conv-kernels are proposed to encode the multilevel contextual information.