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Deep Learning for Classifying of White Blood Cancer

  • Yifan Ding
  • Yujia Yang
  • Yan CuiEmail author
Conference paper
Part of the Lecture Notes in Bioengineering book series (LNBE)

Abstract

Cell classification by computer assistant technique has become a popular topic recently. It is crucial to do early disease diagnosis with less cost. In order to find efficient, high accuracy computer assistant methods, The International Symposium on Biomedical Imaging (ISBI) held the classification of normal vs malignant cells (C-NMC) challenge 2019 for classifying B-ALL white blood cancer microscopic images. Our team deploys several state-of-the-art CNN architectures and uses an ensemble method named stacking to boost the final prediction performance, which helped to get the eighth place in the preliminary phase and tenth place in the final phase. Weighted F1 scores evaluated on our model were 0.8674 and 0.8552, for the preliminary and final phase, respectively. It is a relatively reasonable result in such an expertise required task.

Keywords

Deep learning Ensemble model CNN 

References

  1. 1.
    Pui, C.H.: Acute lymphoblastic leukemia. Pediatr. Clin. North Am. 44(4), 831 (1997)CrossRefGoogle Scholar
  2. 2.
    Nazlibilek, S., Karacor, D., Ercan, T., et al.: Automatic segmentation, counting, size determination and classification of white blood cells. Measurement 55, 58–65 (2014)CrossRefGoogle Scholar
  3. 3.
    Wang, D., Khosla, A., Gargeya, R., et al.: Deep learning for identifying metastatic breast cancer (2016)Google Scholar
  4. 4.
    Grewal, M., Srivastava, M.M., Kumar, P., et al.: RADNET: radiologist level accuracy using deep learning for HEMORRHAGE detection in CT scans (2017)Google Scholar
  5. 5.
    Esteva, A., Kuprel, B., Novoa, R.A., et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639), 115–118 (2017)CrossRefGoogle Scholar
  6. 6.
    Kamnitsas, K., Ledig, C., Newcombe, V.F.J., et al.: Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med. Image Anal. (2016). S1361841516301839Google Scholar
  7. 7.
    Voets, M., Møllersen, K., Bongo, L.A.: Replication study: development and validation of deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs (2018)Google Scholar
  8. 8.
    Rajpurkar, P., Hannun, A.Y., Haghpanahi, M., et al.: Cardiologist-level Arrhythmia detection with convolutional neural networks (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 (ICVGIP), India, December 2016Google Scholar
  10. 10.
    Duggal, R., Gupta, A., Gupta, R.: Segmentation of overlapping/touching white blood cell nuclei using artificial neural networks. CME Series on Hemato-Oncopathology, All India Institute of Medical Sciences (AIIMS), New Delhi, India, July 2016Google Scholar
  11. 11.
    Duggal, R., Gupta, A., Gupta, R., Mallick, P.: SD-layer: stain deconvolutional layer for CNNs in medical microscopic imaging. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D., Duchesne, S. (eds.) Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017. Lecture Notes in Computer Science, Part III, LNCS 10435, pp. 435–443. Springer, Cham.  https://doi.org/10.1007/978-3-319-66179-7_50CrossRefGoogle Scholar
  12. 12.
    Deng, J., Dong, W., Socher, R., et al.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition. IEEE (2009)Google Scholar
  13. 13.
    Rajpurkar, P., Irvin, J., Zhu, K., et al.: CheXNet: radiologist-level pneumonia detection on chest X-rays with deep learning (2017)Google Scholar
  14. 14.
    Deng, L., Yu, D.: Deep learning: methods and applications. Found. Trends Signal Process. 7(3), 197–387 (2014)CrossRefGoogle Scholar
  15. 15.
    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
  16. 16.
    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 (IMW), India, March 2017Google Scholar
  17. 17.
    Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: CVPR (2016)Google Scholar
  18. 18.
    Huang, G., Liu, Z., Weinberger, K.Q., Maaten, L.: Densely connected convolutional networks. In: CVPR (2017)Google Scholar
  19. 19.
    Szegedy, C., Ioffe, S., Vanhoucke, V.: Inception-v4, Inception-ResNet and the impact of residual connections on learning. In: CoRR (2016). arXiv:1602.07261
  20. 20.
    Breiman, L.: Stacked regressions. Mach. Learn. 24(1), 49–64 (1996)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Zhuhai 4DAGE Tech Co., Ltd.ZhuhaiChina
  2. 2.Jinan UniversityGuangzhouChina
  3. 3.Wuyi UniversityJiangmenChina

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