DeepMEN: Multi-model Ensemble Network for B-Lymphoblast Cell Classification

  • Fenrui XiaoEmail author
  • Ruifeng Kuang
  • Zhonghong Ou
  • Baiqiao Xiong
Conference paper
Part of the Lecture Notes in Bioengineering book series (LNBE)


In recent years, convolutional neural networks have achieved great results in many image classification tasks, including the field of medical image processing. However, for leukemic B-lymphoblast cells, the traditional convolutional neural network has some problems. On the one hand, there is a strong individual difference in the cell image, that is, there is a big difference between the cells of different subjects, and this difference may exceed the difference between abnormal cells and normal cells. On the other hand, the two cell types appear very similar. People without expertise can’t tell if a cell is abnormal. In order to solve the above problems, we proposed Deep Multi-model Ensemble Network (DeepMEN). We trained a series of deep learning models and selected the six models with the most potential as candidates. We trained the six models respectively and used the model ensemble technology to fuse the six models. In addition, we use the pseudo-label and Test Time Augmentator (TTA) to reduce covariance shifts caused by individual differences. Finally, we obtained a weighted F1-score of 0.903 in the preliminary test set and 0.8856 in the final test set.


Deep learning Covariance shift Model ensemble 


  1. 1.
    Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images. Technical report, Citeseer (2009)Google Scholar
  2. 2.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)Google Scholar
  3. 3.
    Fu, H., Xu, Y., Wong, D.W.K., Liu, J.: Retinal vessel segmentation via deep learning network and fully-connected conditional random fields. In: 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), pp. 698–701 (2016).
  4. 4.
    Huang, X., Shan, J., Vaidya, V.: Lung nodule detection in CT using 3D convolutional neural networks. In: 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), pp. 379–383 (2017).
  5. 5.
    Habibzadeh, M., Jannesari, M., Rezaei, Z., Baharvand, H., Totonchi, M.: Automatic white blood cell classification using pre-trained deep learning models: ResNet and inception. In: Tenth International Conference on Machine Vision (ICMV 2017), vol. 10696, p. 1069612. International Society for Optics and Photonics (2018)Google Scholar
  6. 6.
    Yosinski, J., Clune, J., Nguyen, A., Fuchs, T., Lipson, H.: Understanding neural networks through deep visualization (2015). arXiv:1506.06579
  7. 7.
    Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)CrossRefGoogle Scholar
  8. 8.
    Lee, D.H.: Pseudo-label: the simple and efficient semi-supervised learning method for deep neural networks. In: Workshop on Challenges in Representation Learning, ICML, vol. 3, p. 2 (2013)Google Scholar
  9. 9.
    Young, I.T.: The classification of white blood cells. IEEE Trans. Biomed. Eng. 4, 291–298 (1972)CrossRefGoogle Scholar
  10. 10.
    Faraki, M., Harandi, M.T., Wiliem, A., Lovell, B.C.: Fisher tensors for classifying human epithelial cells. Pattern Recognit. 47(7), 2348–2359 (2014)CrossRefGoogle Scholar
  11. 11.
    Kong, X., Li, K., Cao, J., Yang, Q., Wenyin, L.: HEp-2 cell pattern classification with discriminative dictionary learning. Pattern Recognit. 47(7), 2379–2388 (2014)CrossRefGoogle Scholar
  12. 12.
    LeCun, Y., Bottou, L., Bengio, Y., Haffner, P., et al.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)CrossRefGoogle Scholar
  13. 13.
    Bayramoglu, N., Kannala, J., Heikkilä, J.: Human epithelial type 2 cell classification with convolutional neural networks. In: 2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE), pp. 1–6. IEEE (2015)Google Scholar
  14. 14.
    Lei, H., Han, T., Zhou, F., Yu, Z., Qin, J., Elazab, A., Lei, B.: A deeply supervised residual network for HEp-2 cell classification via cross-modal transfer learning. Pattern Recognit. 79, 290–302 (2018)CrossRefGoogle Scholar
  15. 15.
    Xue, Y., Ray, N.: Output encoding by compressed sensing for cell detection with deep convnet. In: Workshops at the Thirty-Second AAAI Conference on Artificial Intelligence (2018)Google Scholar
  16. 16.
    Duggal, R., Gupta, A., Gupta, R., Mallick, P.: SD-layer: stain deconvolutional layer for CNNs in medical microscopic imaging. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 435–443. Springer (2017)Google Scholar
  17. 17.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)Google Scholar
  18. 18.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014). arXiv:1409.1556
  19. 19.
    Zhang, H., Xue, J., Dana, K.: Deep ten: texture encoding network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 708–717 (2017)Google Scholar
  20. 20.
    Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)Google Scholar
  21. 21.
    Chen, Y., Li, J., Xiao, H., Jin, X., Yan, S., Feng, J.: Dual path networks. In: Advances in Neural Information Processing Systems, pp. 4467–4475 (2017)Google Scholar
  22. 22.
    Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)Google Scholar
  23. 23.
    Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A.: Inception-v4, inception-ResNet and the impact of residual connections on learning. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)Google Scholar
  24. 24.
    Gupta, R., Mallick, P., Duggal, R., Gupta, A., Sharma, O.: Stain color normalization and segmentation of plasma cells in microscopic images as a prelude to development of computer assisted automated disease diagnostic tool in multiple myeloma. Clin. Lymphoma Myeloma Leukemia 17(1), e99 (2017)Google Scholar
  25. 25.
    Duggal, R., Gupta, A., Gupta, R., Wadhwa, M., Ahuja, C.: Overlapping cell nuclei segmentation in microscopic images using deep belief networks. In: Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing, p. 82. ACM (2016)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Fenrui Xiao
    • 1
    Email author
  • Ruifeng Kuang
    • 1
  • Zhonghong Ou
    • 1
  • Baiqiao Xiong
    • 1
  1. 1.Beijing University of Posts and TelecommunicationsBeijingChina

Personalised recommendations