Automated pulmonary nodule detection: High sensitivity with few candidates

Abstract

Automated pulmonary nodule detection plays an important role in lung cancer diagnosis. In this paper, we propose a pulmonary detection framework that can achieve high sensitivity with few candidates. First, the Feature Pyramid Network (FPN), which leverages multi-level features, is applied to detect nodule candidates that cover almost all true positives. Then redundant candidates are removed by a simple but effective Conditional 3-Dimensional Non-Maximum Suppression (Conditional 3D-NMS). Moreover, a novel Attention 3D CNN (Attention 3D-CNN) which efficiently utilizes contextual information is proposed to further remove the overwhelming majority of false positives. The proposed method yields a sensitivity of 95.8% at 2 false positives per scan on the LUng Nodule Analysis 2016 (LUNA16) dataset, which is competitive compared to the current published state-of-the-art methods.