Accurate detection of pulmonary nodules with high sensitivity and specificity is essential for automatic lung cancer diagnosis from CT scans. Although many deep learning-based algorithms make great progress for improving the accuracy of nodule detection, the high false positive rate is still a challenging problem which limited the automatic diagnosis in routine clinical practice. In this paper, we propose a novel pulmonary nodule detection framework based on a 3D Feature Pyramid Network (3DFPN) to improve the sensitivity of nodule detection by employing multi-scale features to increase the resolution of nodules, as well as a parallel top-down path to transit the high-level semantic features to complement low-level general features. Furthermore, a High Sensitivity and Specificity (HS$^2$) network is introduced to eliminate the falsely detected nodule candidates by tracking the appearance changes in continuous CT slices of each nodule candidate. The proposed framework is evaluated on the public Lung Nodule Analysis (LUNA16) challenge dataset. Our method is able to accurately detect lung nodules at high sensitivity and specificity and achieves $90.4\%$ sensitivity with 1/8 false positive per scan which outperforms the state-of-the-art results $15.6\%$.

本研究提出一种基于3D Feature Pyramid Network的肺部结节检测框架，采用多尺度特征和平行自上而下路径来改善结节检测的灵敏度，同时引入了高灵敏度和特异度 (HS$^2$) 网络，通过跟踪连续 CT 切片中每个结节候选物的外观变化来消除错误检测的结节候选物，该方法在LUNA16数据集上表现出优异的检测效果。

3DFPN-HS$^2$: 基于3D特征金字塔网络的高灵敏度和高特异性肺结节检测