Advertisement

Classification of Breast Cancer Histopathological Images using Convolutional Neural Networks with Hierarchical Loss and Global Pooling

  • Zeya WangEmail author
  • Nanqing Dong
  • Wei Dai
  • Sean D. Rosario
  • Eric P. Xing
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10882)

Abstract

Deep learning-based computer-aided diagnosis (CAD) has been gaining popularity for analyzing histopathological images. However, there has been limited work that addresses the problem of accurately classifying breast biopsy tissue with hematoxylin and eosin stained images into different histological grades. We propose a system which can automatically classify breast cancer histology images into four classes, namely normal tissues, benign lesion, in situ carcinoma and invasive carcinoma. Our framework uses a Convolutional Neural Network (CNN) with a hierarchical loss, where failing to distinguish between carcinoma and non-carcinoma is penalized more than failing to distinguish between normal and benign or between in situ and invasive carcinoma. The network also includes a patch-wise design with global pooling directly on input images. By incorporating the hierarchical and global information of the input images, our framework can outperform the previous system by a large margin.

Keywords

Convolutional Neural Networks Image classification Histopathology Breast cancer Hierarchical loss 

References

  1. 1.
    Siegel, R.L., Miller, K.D., Jemal, A.: Cancer statistics, 2016. CA Cancer J. Clin. 66(1), 7–30 (2016)CrossRefGoogle Scholar
  2. 2.
    American Cancer Society: Breast Cancer Facts & Figures 2017–2018. American Cancer Society, Inc., Atlanta (2017)Google Scholar
  3. 3.
    Araújo, T., Aresta, G., Castro, E., Rouco, J., Aguiar, P., Eloy, C., Polónia, A., Campilho, A.: Classification of breast cancer histology images using convolutional neural networks. PloS One 12(6), e0177544 (2017)CrossRefGoogle Scholar
  4. 4.
    Gurcan, M.N., Boucheron, L.E., Can, A., Madabhushi, A., Rajpoot, N.M., Yener, B.: Histopathological image analysis: a review. IEEE Rev. Biomed. Eng. 2, 147–171 (2009)CrossRefGoogle Scholar
  5. 5.
    Litjens, G., Kooi, T., Bejnordi, B.E., Setio, A.A.A., Ciompi, F., Ghafoorian, M., van der Laak, J.A.W.M., van Ginneken, B., Sánchez, C.I.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)CrossRefGoogle Scholar
  6. 6.
    Ehteshami Bejnordi, B., Veta, M., van Diest, P.J., et al.: Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA 318(22), 2199–2210 (2017)CrossRefGoogle Scholar
  7. 7.
    Wang, D., Khosla, A., Gargeya, R., Irshad, H., Beck, A.H.: Deep learning for identifying metastatic breast cancer. arXiv preprint arXiv:1606.05718 (2016)
  8. 8.
    Macenko, M., Niethammer, M., Marron, J.S., Borland, D., Woosley, J.T., Guan, X., Schmitt, C., Thomas, N.E.: A method for normalizing histology slides for quantitative analysis. In: IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009, pp. 1107–1110. IEEE (2009)Google Scholar
  9. 9.
    Veta, M., van Diest, P.J., Willems, S.M., Wang, H., Madabhushi, A., Cruz-Roa, A., Gonzalez, F., Larsen, A.B., Vestergaard, J.S., Dahl, A.B., et al.: Assessment of algorithms for mitosis detection in breast cancer histopathology images. Med. Image Anal. 20(1), 237 (2015)CrossRefGoogle Scholar
  10. 10.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
  11. 11.
    Wu, C., Tygert, M., LeCun, Y.: Hierarchical loss for classification. arXiv preprint arXiv:1709.01062 (2017)
  12. 12.
    Liu, W., Rabinovich, A., Berg, A.C.: Parsenet: Looking wider to see better. In: CoRR. Citeseer (2015)Google Scholar
  13. 13.
    Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., Lecun, Y.: Overfeat: integrated recognition, localization and detection using convolutional networks. In: International Conference on Learning Representations (ICLR2014), CBLS, April 2014Google Scholar
  14. 14.
    Komura, D., Ishikawa, S.: Machine learning methods for histopathological image analysis. arXiv preprint arXiv:1709.00786 (2017)
  15. 15.
    Pizer, S.M., Amburn, E.P., Austin, J.D., Cromartie, R., Geselowitz, A., Greer, T., ter Haar Romeny, B., Zimmerman, J.B., Zuiderveld, K.: Adaptive histogram equalization and its variations. Comput. Vis. Graph. Image Process. 39(3), 355–368 (1987)CrossRefGoogle Scholar
  16. 16.
    Sutskever, I., Martens, J., Dahl, G., Hinton, G.: On the importance of initialization and momentum in deep learning. In: International Conference on Machine Learning, pp. 1139–1147 (2013)Google Scholar
  17. 17.
    Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 248–255. IEEE (2009)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Petuum Inc.PittsburghUSA
  2. 2.Rice UniversityHoustonUSA
  3. 3.Cornell UniversityIthacaUSA

Personalised recommendations