  The early detection and early diagnosis of lung cancer are crucial to improve
the survival rate of lung cancer patients. Pulmonary nodules detection results
have a significant impact on the later diagnosis. In this work, we propose a
new network named RUN to complete nodule detection in a single step by
bypassing the candidate selection. The system introduces the shortcut of the
residual network to improve the traditional U-Net, thereby solving the
disadvantage of poor results due to its lack of depth. Furthermore, we compare
the experimental results with the traditional U-Net. We validate our method in
LUng Nodule Analysis 2016 (LUNA16) Nodule Detection Challenge. We acquire a
sensitivity of 90.90% at 2 false positives per scan and therefore achieve
better performance than the current state-of-the-art approaches.
