Advertisement

CP-MCNN: Multi-label Chest X-ray Diagnostic Based on Confidence Predictor and CNN

  • Huazhen Wang
  • Junlong Liu
  • Sisi Lai
  • Nengguang Wu
  • Jixiang DuEmail author
Conference paper
  • 373 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11637)

Abstract

Chest X-ray as a sensing mode is worthwhile to be paid attention in terms of its conversation and prevalence, and it as a typical multi-label problem where each example is represented by a single instance while associated with a set of labels simultaneously. Early researches on chest X-ray mainly using Convolutional Neural Network (CNN), although it has outperformance in experiment, diagnosis of chest x-ray as a typical high-risk problem, CNN lacks confidence evaluation its output to make a judgment. To solve this problem, we propose a new framework of Confidence Prediction-Multi-label Convolutional Neural Network (CP-MCNN) that plugs MCNN into Confidence Predictor. It can provide calibrated confidential evaluation for MCNN. On chestx-ray14 dataset, the experimental results show that CP-MCNN performs better than MCNN in terms of Sub-accuracy, Hamming-loss, Ranking-loss and Average Precision. Moreover, CP-MCNN can provide well-calibrated confidence prediction on chest X-ray sensor picture in order to enhance its reliability and interpretability.

Keywords

Sensor Chest X-ray Conformal predictor CNN Multi-label 

Notes

Acknowledgments

This work is supported by National Natural Science Foundation of China under Grant No. 61673186, the Natural Science Foundation of Fujian Province in China under Grant No. 2012J01274.

References

  1. 1.
    Balasubramanian, V., Ho, S.S., Vovk, V.: Conformal prediction for reliable machine learning: theory, adaptations and applications (2014)Google Scholar
  2. 2.
    Gao, L., Wang, J., Fan, Y., Chen, N.: Robust visual tracking based on convolutional neural networks and conformal predictor. Acta Optica Sinica 37(8), 0815003 (2017)CrossRefGoogle Scholar
  3. 3.
    Madjarov, G., Kocev, D., Gjorgjevikj, D., Džeroski, S.: An extensive experimental comparison of methods for multi-label learning. Pattern Recogn. 45(9), 3084–3104 (2012)CrossRefGoogle Scholar
  4. 4.
    Li, Z., Zheng, Y., Zhang, C., Shi, Z.: Combining deep feature and multi-label classification for semantic image annotation. J. Comput.-Aided Des. Comput. Graph. 30(2), 318 (2018)Google Scholar
  5. 5.
    Dewey, M., Kachelrieß, M.: Fundamentals of X-ray computed tomography: acquisition and reconstruction. In: Sack, I., Schaeffter, T. (eds.) Quantification of Biophysical Parameters in Medical Imaging, pp. 325–339. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-65924-4_14CrossRefGoogle Scholar
  6. 6.
    Mojoo, J., Kurosawa, K., Kurita, T.: Deep CNN with graph laplacian regularization for multi-label image annotation. In: Karray, F., Campilho, A., Cheriet, F. (eds.) ICIAR 2017. LNCS, vol. 10317, pp. 19–26. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-59876-5_3CrossRefGoogle Scholar
  7. 7.
    Abiyev, R.H., Ma’aitah, M.K.S.: Deep convolutional neural networks for chest diseases detection (2018)CrossRefGoogle Scholar
  8. 8.
    Rajpurkar, P., et al.: CheXNet: radiologist-level pneumonia detection on chest x-rays with deep learning 1711, 1 November 2017. http://adsabs.harvard.edu/abs/2017arXiv171105225R
  9. 9.
    Schmidhuber, J.: Deep learning in neural networks: an overview (2014)Google Scholar
  10. 10.
    Shafer, G., Vovk, V.: A tutorial on conformal prediction. J. Mach. Learn. Res. 9, 371–421 (2008). \(<\)Go to ISI\(>\)://000256642000002Google Scholar
  11. 11.
    Iwata, T., Ghahramani, Z.: Improving output uncertainty estimation and generalization in deep learning via neural network Gaussian processes (2017)Google Scholar
  12. 12.
    Tsoumakas, G., Katakis, I.: Multi-label classification: an overview. Int. J. Data Warehouse. Mining (IJDWM) 3(3), 1–13 (2007)CrossRefGoogle Scholar
  13. 13.
    Vovk, V., Gammerman, A., Shafer, G.: Algorithmic Learning in a Random World. Springer, New York (2005).  https://doi.org/10.1007/b106715CrossRefzbMATHGoogle Scholar
  14. 14.
    Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., Summers, R.M.: Chestx-ray8: hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3462–3471 (2017)Google Scholar
  15. 15.
    Zhang, M., Zhou, Z.: A review on multi-label learning algorithms (2013)Google Scholar
  16. 16.
    Zhao, H.J.W.C.C.: Pulmonary tuberculosis detection model of chest x-ray images using convolutional neural network, 8 July 2018Google Scholar
  17. 17.
    Zhu, J., Liao, S., Yi, D., Lei, Z., Li, S.Z.: Multi-label CNN based pedestrian attribute learning for soft biometrics. In: International Conference on Biometrics, pp. 535–540 (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Huazhen Wang
    • 1
  • Junlong Liu
    • 1
  • Sisi Lai
    • 1
  • Nengguang Wu
    • 1
  • Jixiang Du
    • 1
    Email author
  1. 1.Huaqiao UniversityXiamenChina

Personalised recommendations