Nuclei Classification Using Dual View CNNs with Multi-crop Module in Histology Images

  • Xiang Li
  • Wei LiEmail author
  • Mengmeng Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11257)


Histopathology image diagnostic technique is a quite common requirement; however, cell nuclei classification is still one of key challenge due to complex tissue structure and diversity of nuclear morphology. Cell nuclei categories are often defined by contextual information, including central nucleus and surrounding background. In this paper, we propose a Dual-View Convolutional Neural Networks (DV-CNNs) that captures contextual contents from different views. The DV-CNNs are composed of two independent pathways, one for global region and another for center local region. Noted that each pathway with “multi-crop module” can extract five different feature regions. Common networks do not fully utilize the local information, but the designed cropping module catches information for more complete features. In experiments, two pipelines are complementary to each other in score fusion. To verify the performance in proposed framework, it is evaluated on a colorectal adenocarcinoma image database with more than 20,000 nuclei. Compared with existing methods, our proposed DV-CNNs with multi-crop module demonstrate better performance.


Histopathology image analysis Convolutional neural network Cell nuclei classification 


  1. 1.
    Adur, J., et al.: Colon adenocarcinoma diagnosis in human samples by multicontrast nonlinear optical microscopy of hematoxylin and eosin stained histological sections. J. Cancer Therapy 5(13), 1259–1269 (2014)CrossRefGoogle Scholar
  2. 2.
    Bayramoglu, N., Heikkilä, J.: Transfer learning for cell nuclei classification in histopathology images. In: Hua, G., Jégou, H. (eds.) ECCV 2016 Part III. LNCS, vol. 9915, pp. 532–539. Springer, Cham (2016). Scholar
  3. 3.
    Hand, D.J., Till, R.J.: A simple generalisation of the area under the roc curve for multiple class classification problems. Mach. Learn. 45(2), 171–186 (2001)CrossRefGoogle Scholar
  4. 4.
    Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on International Conference on Machine Learning, pp. 448–456 (2015)Google Scholar
  5. 5.
    Irshad, H., Veillard, A., Roux, L., Racoceanu, D.: Methods for nuclei detection, segmentation, and classification in digital histopathology: a review-current status and future potential. IEEE Rev. Biomed. Eng. 7(1–5), 97–114 (2014)CrossRefGoogle Scholar
  6. 6.
    Kingma, D., Ba, J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations (2014)Google Scholar
  7. 7.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: International Conference on Neural Information Processing Systems, pp. 1097–1105 (2012)Google Scholar
  8. 8.
    Madabhushi, A., Lee, G.: Image analysis and machine learning in digital pathology: challenges and opportunities. Med. Image Anal. 33, 170–175 (2016)CrossRefGoogle Scholar
  9. 9.
    Malon, C.D., Cosatto, E.: Classification of mitotic figures with convolutional neural networks and seeded blob features. J. Pathol. Inform. 4(1), 9 (2013)CrossRefGoogle Scholar
  10. 10.
    Murthy, V., Hou, L., Samaras, D., Kurc, T.M., Saltz, J.H.: Center-focusing multi-task CNN with injected features for classification of Glioma nuclear images. In: Applications of Computer Vision, pp. 834–841. IEEE (2016)Google Scholar
  11. 11.
    Nguyen, K., Bredno, J., Knowles, D.A.: Using contextual information to classify nuclei in histology images. In: IEEE International Symposium on Biomedical Imaging, pp. 995–998 (2015)Google Scholar
  12. 12.
    Rajesh, K., Rajeev, S., Subodh, S.: Detection and classification of cancer from microscopic biopsy images using clinically significant and biologically interpretable features. J. Med. Eng. 2015, 457906 (2015)Google Scholar
  13. 13.
    Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. Off. J. Int. Neural Netw. Soc. 61, 85 (2014)CrossRefGoogle Scholar
  14. 14.
    Sirinukunwattana, K., Shan, E.A.R., Tsang, Y.W., Snead, D.R.J., Cree, I.A., Rajpoot, N.M.: Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE Trans. Med. Imaging 35(5), 1196–1206 (2016)CrossRefGoogle Scholar
  15. 15.
    Veta, M., Pluim, J.P.W., Diest, P.J.V., Viergever, M.A.: Breast cancer histopathology image analysis: a review. IEEE Trans. Bio-Med. Eng. 61(5), 1400–1411 (2014)CrossRefGoogle Scholar
  16. 16.
    Wang, H., Cruzroa, A., Gilmore, H., Feldman, M., Tomaszewski, J., Madabhushi, A.: Cascaded ensemble of convolutional neural networks and handcrafted features for mitosis detection. In: SPIE Medical Imaging, pp. 90410B–90410B-10 (2015)Google Scholar
  17. 17.
    Yu, Y., Lin, H., Meng, J., Wei, X., Guo, H., Zhao, Z.: Deep transfer learning for modality classification of medical images. Information 8(3), 91 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.College of Information Science and TechnologyBeijing University of Chemical TechnologyBeijingChina

Personalised recommendations