Advertisement

DeepEM: Deep 3D ConvNets with EM for Weakly Supervised Pulmonary Nodule Detection

  • Wentao ZhuEmail author
  • Yeeleng S. Vang
  • Yufang Huang
  • Xiaohui Xie
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11071)

Abstract

Recently deep learning has been witnessing widespread adoption in various medical image applications. However, training complex deep neural nets requires large-scale datasets labeled with ground truth, which are often unavailable in many medical image domains. For instance, to train a deep neural net to detect pulmonary nodules in lung computed tomography (CT) images, current practice is to manually label nodule locations and sizes in many CT images to construct a sufficiently large training dataset, which is costly and difficult to scale. On the other hand, electronic medical records (EMR) contain plenty of partial information on the content of each medical image. In this work, we explore how to tap this vast, but currently unexplored data source to improve pulmonary nodule detection. We propose DeepEM, a novel deep 3D ConvNet framework augmented with expectation-maximization (EM), to mine weakly supervised labels in EMRs for pulmonary nodule detection. Experimental results show that DeepEM can lead to 1.5% and 3.9% average improvement in free-response receiver operating characteristic (FROC) scores on LUNA16 and Tianchi datasets, respectively, demonstrating the utility of incomplete information in EMRs for improving deep learning algorithms (https://github.com/uci-cbcl/DeepEM-for-Weakly-Supervised-Detection.git).

Keywords

Deep 3D convolutional nets Weakly supervised detection DeepEM (deep 3D ConvNets with EM) Pulmonary nodule detection 

References

  1. 1.
    Bilen, H., et al.: Weakly supervised deep detection networks. In: CVPR (2016)Google Scholar
  2. 2.
    Ding, J., Li, A., Hu, Z., Wang, L.: Accurate pulmonary nodule detection in computed tomography images using deep convolutional neural networks. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 559–567. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-66179-7_64CrossRefGoogle Scholar
  3. 3.
    Dou, Q., Chen, H., Jin, Y., Lin, H., Qin, J., Heng, P.-A.: Automated pulmonary nodule detection via 3D ConvNets with online sample filtering and hybrid-loss residual learning. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 630–638. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-66179-7_72CrossRefGoogle Scholar
  4. 4.
    Feng, X., Yang, J., Laine, A.F., Angelini, E.D.: Discriminative localization in CNNs for weakly-supervised segmentation of pulmonary nodules. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 568–576. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-66179-7_65CrossRefGoogle Scholar
  5. 5.
    Hwang, S., Kim, H.-E.: Self-transfer learning for weakly supervised lesion localization. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 239–246. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46723-8_28CrossRefGoogle Scholar
  6. 6.
    Jacob, C., et al.: Automatic detection of subsolid pulmonary nodules in thoracic computed tomography images. Med. Image Anal. 18, 374–384 (2014)CrossRefGoogle Scholar
  7. 7.
    Jesson, A., Guizard, N., Ghalehjegh, S.H., Goblot, D., Soudan, F., Chapados, N.: CASED: curriculum adaptive sampling for extreme data imbalance. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 639–646. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-66179-7_73CrossRefGoogle Scholar
  8. 8.
    Liao, F., et al.: Evaluate the malignancy of pulmonary nodules using the 3d deep leaky noisy-or network. arXiv preprint (2017)Google Scholar
  9. 9.
    Lopez Torres, E., et al.: Large scale validation of the M5L lung cad on heterogeneous ct datasets. Med. Phys. 42, 1477–1489 (2015)CrossRefGoogle Scholar
  10. 10.
    Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-24574-4_28CrossRefGoogle Scholar
  11. 11.
    Setio, A.A.A., et al.: Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks. IEEE TMI 35, 1160–1169 (2016)Google Scholar
  12. 12.
    Setio, A.A.A., et al.: Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the luna16 challenge. Med. Image Anal. 42, 1–13 (2017)CrossRefGoogle Scholar
  13. 13.
    Tang, H., Kim, D., Xie, X.: Automated pulmonary nodule detection using 3D deep convolutional neural networks. In: ISBI (2018)Google Scholar
  14. 14.
    Zhu, W., Liu, C., Fan, W., Xie, X.: Deeplung: deep 3d dual path nets for automated pulmonary nodule detection and classification. In: IEEE WACV (2018)Google Scholar
  15. 15.
    Zhu, W., Lou, Q., Vang, Y.S., Xie, X.: Deep multi-instance networks with sparse label assignment for whole mammogram classification. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 603–611. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-66179-7_69CrossRefGoogle Scholar
  16. 16.
    Zhu, W., et al.: Adversarial deep structured nets for mass segmentation from mammograms. In: IEEE ISBI (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Wentao Zhu
    • 1
    Email author
  • Yeeleng S. Vang
    • 1
  • Yufang Huang
    • 2
  • Xiaohui Xie
    • 1
  1. 1.University of CaliforniaIrvineUSA
  2. 2.Lenovo ResearchBeijingChina

Personalised recommendations