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.
@article{arxiv.1805.11856,
title = {RUN:Residual U-Net for Computer-Aided Detection of Pulmonary Nodules without Candidate Selection},
author = {Tian Lan and Yuanyuan Li and Jonah Kimani Murugi and Yi Ding and Zhiguang Qin},
journal= {arXiv preprint arXiv:1805.11856},
year = {2018}
}
Comments
15 pages, 5 figures, manuscript for Neurocomputing