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Automated Pulmonary Nodule Detection: High Sensitivity with Few Candidates

  • Bin Wang
  • Guojun Qi
  • Sheng TangEmail author
  • Liheng Zhang
  • Lixi Deng
  • Yongdong Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11071)

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.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Bin Wang
    • 1
    • 3
  • Guojun Qi
    • 2
  • Sheng Tang
    • 1
    Email author
  • Liheng Zhang
    • 2
  • Lixi Deng
    • 1
    • 3
  • Yongdong Zhang
    • 1
  1. 1.Key Lab of Intelligent Information Processing, Institute of Computing TechnologyChinese Academy of SciencesBeijingChina
  2. 2.University of Central FloridaOrlandoUSA
  3. 3.University of the Chinese Academy of SciencesBeijingChina

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