Dense Attention Module For Accurate Pulmonary Nodule Detection

Lung cancer has been the leading death cause in modern society. Early detection of pulmonary nodules can significantly improve the survival rate of lung cancer. In this paper, we propose a novel pulmonary nodule detection framework and a novel 3D dense attention module (DAM) which can efficiently exploit the abundant 3D spatial features. The attention module, which integrates the improved dense block and the conv attention block, focuses on three dimensions, plane attention, depth attention, and channel attention. And the whole framework consists of two phases: Nodule Candidate Generation (NCG) and False Positive Reduction (FPR). In NCG phase, we construct a detection network based on DAM. Due to the wide distribution of the nodule diameters, we propose a 3D Feature Pyramid Network (3DFPN) to better handle the scale-varying problem. In FPR phase, we design a 3D DCNN to erase the false positives. Sliding-window based data augment methods are adopted to deal with the unbalance problem of the data. Comprehensive experiments show that our scheme outperforms the existing methods.