3DFPN-HS\(^2\): 3D Feature Pyramid Network Based High Sensitivity and Specificity Pulmonary Nodule Detection

  • Jingya Liu
  • Liangliang Cao
  • Oguz Akin
  • Yingli TianEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11769)


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 \(15.6\%\).


Lung nodule detection False positive reduction CT Deep learning 



This material is based upon work supported by the National Science Foundation under award number IIS-1400802 and Memorial Sloan Kettering Cancer Center Support Grant/Core Grant P30 CA008748. Oguz Akin, MD serves as a scientific advisor for Ezra AI, Inc., which is unrelated to the research being reported.

Supplementary material

490281_1_En_57_MOESM1_ESM.pdf (4 mb)
Supplementary material 1 (pdf 4146 KB)


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Jingya Liu
    • 1
  • Liangliang Cao
    • 2
    • 3
  • Oguz Akin
    • 4
  • Yingli Tian
    • 1
    Email author
  1. 1.The City College of New YorkNew YorkUSA
  2. 2.UMass CICSAmherstUSA
  3. 3.Google AINew YorkUSA
  4. 4.Memorial Sloan Kettering Cancer CenterNew YorkUSA

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