Advertisement

Invasive Cancer Detection Utilizing Compressed Convolutional Neural Network and Transfer Learning

  • Bin Kong
  • Shanhui SunEmail author
  • Xin Wang
  • Qi Song
  • Shaoting ZhangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11071)

Abstract

Identification of invasive cancer in Whole Slide Images (WSIs) is crucial for tumor staging as well as treatment planning. However, the precise manual delineation of tumor regions is challenging, tedious and time-consuming. Thus, automatic invasive cancer detection in WSIs is of significant importance. Recently, Convolutional Neural Network (CNN) based approaches advanced invasive cancer detection. However, computation burdens of these approaches become barriers in clinical applications. In this work, we propose to detect invasive cancer employing a lightweight network in a fully convolution fashion without model ensembles. In order to improve the small network’s detection accuracy, we utilized the “soft labels” of a large capacity network to supervise its training process. Additionally, we adopt a teacher guided loss to help the small network better learn from the intermediate layers of the high capacity network. With this suite of approaches, our network is extremely efficient as well as accurate. The proposed method is validated on two large scale WSI datasets. Our approach is performed in an average time of 0.6 and 3.6 min per WSI with a single GPU on our gastric cancer dataset and CAMELYON16, respectively, about 5 times faster than Google Inception V3. We achieved an average FROC of \(81.1\%\) and \(85.6\%\) respectively, which are on par with Google Inception V3. The proposed method requires less high performance computing resources than state-of-the-art methods, which makes the invasive cancer diagnosis more applicable in the clinical usage.

Notes

Acknowledgements

This work is partially supported by the National Science Foundation under grant IIP-1439695, ABI-1661280 and CNS-1629913.

References

  1. 1.
    Chollet, F., et al.: Xception: deep learning with depthwise separable convolutions. In: CVPR, pp. 1251–1258 (2017)Google Scholar
  2. 2.
    Hinton, G., et al.: Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)
  3. 3.
    Howard, A., et al.: Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)
  4. 4.
    Jia, Y., et al.: Caffe: convolutional architecture for fast feature embedding. In: ACMMM, pp. 675–678. ACM (2014)Google Scholar
  5. 5.
    Kong, B., Zhan, Y., Shin, M., Denny, T., Zhang, S.: Recognizing end-diastole and end-systole frames via deep temporal regression network. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9902, pp. 264–272. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46726-9_31CrossRefGoogle Scholar
  6. 6.
    Kong, B., Wang, X., Li, Z., Song, Q., Zhang, S.: Cancer metastasis detection via spatially structured deep network. In: Niethammer, M., et al. (eds.) IPMI 2017. LNCS, vol. 10265, pp. 236–248. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-59050-9_19CrossRefGoogle Scholar
  7. 7.
    Lin, H., et al.: Scannet: a fast and dense scanning framework for metastatic breast cancer detection from whole-slide images. arXiv preprint arXiv:1707.09597 (2017)
  8. 8.
    Liu, Y., et al.: Detecting cancer metastases on gigapixel pathology images. arXiv preprint arXiv:1703.02442 (2017)
  9. 9.
    Long, J., et al.: Fully convolutional networks for semantic segmentation. In: CVPR, pp. 3431–3440 (2015)Google Scholar
  10. 10.
    Rastegari, M., Ordonez, V., Redmon, J., Farhadi, A.: XNOR-net: imagenet classification using binary convolutional neural networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 525–542. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46493-0_32CrossRefGoogle Scholar
  11. 11.
    Romero, A., et al.: Fitnets: hints for thin deep nets. arXiv preprint arXiv:1412.6550 (2014)
  12. 12.
    Shiraishi, J.: Computer-aided diagnostic scheme for the detection of lung nodules on chest radiographs: localized search method based on anatomical classification. Med. Phys. 33(7), 2642–2653 (2006)CrossRefGoogle Scholar
  13. 13.
    Siegel, R.L., et al.: Cancer statistics, 2017. CA Cancer J. Clin. 67(1), 7–30 (2017).  https://doi.org/10.3322/caac.21387CrossRefGoogle Scholar
  14. 14.
    Szegedy, C., et al.: Going deeper with convolutions. In: CVPR, pp. 1–9 (2015)Google Scholar
  15. 15.
    Szegedy, C., et al.: Rethinking the inception architecture for computer vision. In: CVPR, pp. 2818–2826 (2016)Google Scholar
  16. 16.
    Wang, D., et al.: Deep learning for identifying metastatic breast cancer. arXiv preprint arXiv:1606.05718 (2016)
  17. 17.
    Wu, J., et al.: Quantized convolutional neural networks for mobile devices. In: CVPR, pp. 4820–4828 (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceUNC CharlotteCharlotteUSA
  2. 2.CuraCloud CorporationSeattleUSA

Personalised recommendations