Multi-streams and Multi-features for Cell Classification

  • Xinpeng Xie
  • Yuexiang Li
  • Menglu Zhang
  • Yong Wu
  • Linlin ShenEmail author
Conference paper
Part of the Lecture Notes in Bioengineering book series (LNBE)


With the development of deep learning technique, cell classification has gained increasing interests from the community. Identifying malignant cells in B-ALL white blood cancer microscopic images is challenging, since the normal and malignant cells have similar appearances. Traditional cell identification approach requires experienced pathologists to carefully read the cell images, which is laborious and suffers from inter-observer variations. Hence, the computer aid diagnosis systems for blood disorders, for example, leukemia, are worthwhile to develop. In this paper, we design a multi-stream model to classify the immature leukemic blasts and normal cells. We evaluated the proposed model on the C-NMC 2019 challenge dataset. The experimental results show that a promising result is achieved by our model.


Leukemia cell identification Deep learning network Feature fusion 


  1. 1.
    Bhojwani, D., Yang, J.J., Pui, C.-H.: Biology of childhood acute lymphoblastic leukemia. Pediatr. Clin. 62(1), 47–60 (2015)Google Scholar
  2. 2.
    Roth, H.R., Lee, C.T., Shin, H.C., Seff, A., Kim, L., Yao, J., Lu, L., Summers, R.M.: Anatomy-specific classification of medical images using deep convolutional nets. In: IEEE International Symposium on Biomedical Imaging, pp. 101–104 (2015)Google Scholar
  3. 3.
    Zhang, L., Lu, L., Nogues, I., et al.: DeepPap: deep convolutional networks for cervical cell classification. IEEE J. Biomed. Health Inf. 21(6), 1633–1643 (2017)CrossRefGoogle Scholar
  4. 4.
    Duggal, R., Gupta, A., Gupta, R., et al.: SD-layer: stain deconvolutional layer for CNNs in medical microscopic imaging. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham (2017)CrossRefGoogle Scholar
  5. 5.
    Mourya, S., Kant, S., Kumar, P., Gupta, A., et al.: LeukoNet: DCT-based CNN architecture for the classification of normal versus Leukemic blasts in B-ALL Cancer (2018). arXiv:1810.07961
  6. 6.
    Classification of Normal vs Malignant Cells in B-ALL White Blood Cancer Microscopic Images: ISBI 2019.
  7. 7.
    Gupta, A., Duggal, R., Gupta, R., Kumar, L., Thakkar, N., Satpathy, D.: GCTI-SN: Geometry-Inspired Chemical and Tissue Invariant Stain Normalization of Microscopic Medical Images. Under reviewGoogle Scholar
  8. 8.
    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. In: 16th International Myeloma Workshop, India, March (2017)Google Scholar
  9. 9.
    Duggal, R., Gupta, A., Gupta, R., Wadhwa, M., Ahuja, C.: Overlapping cell nuclei segmentation in microscopic images using deep belief networks. In: Indian Conference on Computer Vision, Graphics and Image Processing, India, December (2016)Google Scholar
  10. 10.
    Duggal, R., Gupta, A., Gupta, R.: Segmentation of overlapping/touching white blood cell nuclei using artificial neural networks. CME Series on HematoOncopathology, All India Institute of Medical Sciences, New Delhi, India, July (2016)Google Scholar
  11. 11.
    He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)Google Scholar
  12. 12.
    Szegedy, C., Vanhoucke, V., Ioffe, S., et al.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)Google Scholar
  13. 13.
    Szegedy, C., Ioffe, S., Vanhoucke, V., et al.: Inception-v4, Inception-ResNet and the impact of residual connections on learning. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)Google Scholar
  14. 14.
    Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization (2014). arXiv:1412.6980

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Xinpeng Xie
    • 1
  • Yuexiang Li
    • 2
  • Menglu Zhang
    • 1
  • Yong Wu
    • 1
  • Linlin Shen
    • 1
    Email author
  1. 1.Computer Vision InstituteShenzhen UniversityShenzhenChina
  2. 2.Youtu Lab, TencentShenzhenChina

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