Automated Pulmonary Nodule Detection via 3D ConvNets with Online Sample Filtering and Hybrid-Loss Residual Learning

  • Qi DouEmail author
  • Hao Chen
  • Yueming Jin
  • Huangjing Lin
  • Jing Qin
  • Pheng-Ann Heng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10435)


In this paper, we propose a novel framework with 3D convolutional networks (ConvNets) for automated detection of pulmonary nodules from low-dose CT scans, which is a challenging yet crucial task for lung cancer early diagnosis and treatment. Different from previous standard ConvNets, we try to tackle the severe hard/easy sample imbalance problem in medical datasets and explore the benefits of localized annotations to regularize the learning, and hence boost the performance of ConvNets to achieve more accurate detections. Our proposed framework consists of two stages: (1) candidate screening, and (2) false positive reduction. In the first stage, we establish a 3D fully convolutional network, effectively trained with an online sample filtering scheme, to sensitively and rapidly screen the nodule candidates. In the second stage, we design a hybrid-loss residual network which harnesses the location and size information as important cues to guide the nodule recognition procedure. Experimental results on the public large-scale LUNA16 dataset demonstrate superior performance of our proposed method compared with state-of-the-art approaches for the pulmonary nodule detection task.



This work was supported by the following grants from the Research Grants Council (Project no. CUHK412513) and the Innovation and Technology Fund (Project no. ITS/041/16) of Hong Kong.


  1. 1.
    Aberle, D., Adams, A., Berg, C., Black, W., Clapp, J., Fagerstrom, R., Gareen, I., Gatsonis, C., Marcus, P., Sicks, J.: Reduced lung-cancer mortality with low-dose computed tomographic screening. N. Engl. J. Med. 365, 395–409 (2011)CrossRefGoogle Scholar
  2. 2.
    Cireşan, D.C., Giusti, A., Gambardella, L.M., Schmidhuber, J.: Mitosis detection in breast cancer histology images with deep neural networks. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8150, pp. 411–418. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-40763-5_51 CrossRefGoogle Scholar
  3. 3.
    Dou, Q., Chen, H., Yu, L., Zhao, L., Qin, J., Wang, D., Mok, V.C., Shi, L., Heng, P.A.: Automatic detection of cerebral microbleeds from mr images via 3d convolutional neural networks. IEEE Trans. Med. Imaging 35(5), 1182–1195 (2016)CrossRefGoogle Scholar
  4. 4.
    Girshick, R.: Fast r-cnn. In: IEEE ICCV, pp. 1440–1448 (2015)Google Scholar
  5. 5.
    He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 630–645. Springer, Cham (2016). doi: 10.1007/978-3-319-46493-0_38 CrossRefGoogle Scholar
  6. 6.
    Jacobs, C., van Rikxoort, E.M., Twellmann, T., Scholten, E.T., de Jong, P.A., Kuhnigk, J.M., et al.: Automatic detection of subsolid pulmonary nodules in thoracic computed tomography images. Med. Image Anal. 18(2), 374–384 (2014)CrossRefGoogle Scholar
  7. 7.
    Murphy, K., van Ginneken, B., Schilham, A.M., et al.: A large-scale evaluation of automatic pulmonary nodule detection in chest ct using local image features and k-nearest-neighbour classification. Med. Image Anal. 13(5), 757–770 (2009)CrossRefGoogle Scholar
  8. 8.
    Setio, A.A.A., Ciompi, F., Litjens, G., Gerke, P., Jacobs, C., van Riel, S.J., Wille, M.M.W., Naqibullah, M., Sánchez, C.I., van Ginneken, B.: Pulmonary nodule detection in ct images: false positive reduction using multi-view convolutional networks. IEEE Trans. Med. Imaging 35(5), 1160–1169 (2016)CrossRefGoogle Scholar
  9. 9.
    Setio, A.A.A., Traverso, A., van Ginneken, B., Jacobs, C., et al.: Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the luna16 challenge. arXiv preprint arXiv:1612.08012 (2016)
  10. 10.
    Shrivastava, A., Gupta, A., Girshick, R.: Training region-based object detectors with online hard example mining. In: IEEE CVPR, pp. 761–769 (2016)Google Scholar
  11. 11.
    Van Ginneken, B., Armato, S.G., de Hoop, B., et al.: Comparing and combining algorithms for computer-aided detection of pulmonary nodules in computed tomography scans: the anode09 study. Med. Image Anal. 14(6), 707–722 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Qi Dou
    • 1
    Email author
  • Hao Chen
    • 1
  • Yueming Jin
    • 1
  • Huangjing Lin
    • 1
  • Jing Qin
    • 2
  • Pheng-Ann Heng
    • 1
  1. 1.Department of Computer Science and EngineeringThe Chinese University of Hong KongHong KongThe People’s Republic of China
  2. 2.Center for Smart Health, School of NursingThe Hong Kong Polytechnic UniversityKowloonHong Kong

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