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Acute Lymphoblastic Leukemia Cells Image Analysis with Deep Bagging Ensemble Learning

  • Ying Liu
  • Feixiao LongEmail author
Conference paper
Part of the Lecture Notes in Bioengineering book series (LNBE)

Abstract

Acute lymphoblastic leukemia (ALL) is a blood cancer that leads to 111,000 death globally in 2015. Recently, diagnosing ALL often involves the microscopic image analysis with the help of deep learning (DL) techniques. However, as most medical related problems, deficiency training samples and minor visual difference between ALL and normal cells make the image analysis task quite challenging. Herein, an augmented image enhanced bagging ensemble learning with elaborately designed training subsets were proposed to tackle the above challenges. The weighted \(F_1\)-scores of the preliminary test set and final test are 0.84 and 0.88, respectively employing our ensemble model predictions and ranked within the top 10% in ISBI-2019 Classification of Normal versus Malignant White Blood Cancer Cells contest. Our results preliminarily demonstrate the efficacy of employing DL based techniques in ALL cells image analysis.

Keywords

All cells classification Deep learning Enhanced bagging ensemble method 

References

  1. 1.
    Disease, G., Incidence, I., Collaborators, P.: Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990–2015: a systematic analysis for the global burden of disease study 2015. Lancet (London, England) 388(10053), 1545–1602 (2016).  https://doi.org/10.1016/S0140-6736(16)31678-6. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5055577/CrossRefGoogle Scholar
  2. 2.
    Duggal, R., Gupta, A., Gupta, R.: Segmentation of overlapping/touching white blood cell nuclei using artificial neural networks. In: CME Series on Hemato-Oncopathology, All India Institute of Medical Sciences (AIIMS). New Delhi, India (2016)Google Scholar
  3. 3.
    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.L., Duchesne, S. (eds.) Medical Image Computing and Computer Assisted Intervention MICCAI 2017. Lecture Notes in Computer Science, pp. 435–443. Springer International Publishing (2017)Google Scholar
  4. 4.
    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, ICVGIP’16, pp. 82:1–82:8. ACM, Guwahati, Assam, India (2016).  https://doi.org/10.1145/3009977.3010043
  5. 5.
    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 review)Google Scholar
  6. 6.
    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 Leuk. 17(1), e99 (2017).  https://doi.org/10.1016/j.clml.2017.03.178. https://www.clinical-lymphoma-myeloma-leukemia.com/article/S2152-2650(17)30468-8/abstractGoogle Scholar
  7. 7.
    Li, C.: Classifying imbalanced data using a bagging ensemble variation (BEV). In: Proceedings of the 45th Annual Southeast Regional Conference. pp. 203–208. ACM-SE 45, ACM (2007).  https://doi.org/10.1145/1233341.1233378
  8. 8.
    Rehman, A., Abbas, N., Saba, T., Rahman, S.I.U., Mehmood, Z., Kolivand, H.: Classification of acute lymphoblastic leukemia using deep learning. Microsc. Res. Tech. 81(11), 1310–1317 (2018).  https://doi.org/10.1002/jemt.23139CrossRefPubMedGoogle Scholar
  9. 9.
    Ruder, S.: An overview of gradient descent optimization algorithms (2016). arXiv:1609.04747 [cs]
  10. 10.
    Shafique, S., Tehsin, S.: Acute lymphoblastic leukemia detection and classification of its subtypes using pretrained deep convolutional neural networks. Technol. Cancer Res. Treat. 17 (2018).  https://doi.org/10.1177/1533033818802789. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6161200/CrossRefGoogle Scholar
  11. 11.
    Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.: Inception-v4, inception-ResNet and the impact of residual connections on learning. In: AAAI Conference on Artificial Intelligence (2017)Google Scholar
  12. 12.
    Tajbakhsh, N., Shin, J.Y., Gurudu, S.R., Hurst, R.T., Kendall, C.B., Gotway, M.B., Liang, J.: Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans. Med. Imag. 35(5), 1299–1312 (2016).  https://doi.org/10.1109/TMI.2016.2535302CrossRefGoogle Scholar
  13. 13.
    Vununu, C., Lee, S.H., Kwon, K.R.: A deep feature extraction method for HEp-2 cell image classification. Electronics 8(1), 20 (2019).  https://doi.org/10.3390/electronics8010020. https://www.mdpi.com/2079-9292/8/1/20CrossRefGoogle Scholar
  14. 14.
    Wang, Q., Wang, J., Zhou, M., Li, Q., Wang, Y.: Spectral-spatial feature-based neural network method for acute lymphoblastic leukemia cell identification via microscopic hyperspectral imaging technology. Biomed. Opt. Express 8(6), 3017–3028 (2017).  https://doi.org/10.1364/BOE.8.003017. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5480446/CrossRefGoogle Scholar
  15. 15.
    Yu, L., Chen, H., Dou, Q., Qin, J., Wang, P.A.: Automated melanoma recognition in dermoscopy images via very deep residual networks. IEEE Trans. Med. Imag. 36, 994–1004 (2017)CrossRefGoogle Scholar
  16. 16.
    Zhang, J., Xie, Y., Wu, Q., Xia, Y.: Skin lesion classification in dermoscopy images using synergic deep learning. In: Medical Image Computing and Computer-Assisted Intervention-MICCAI 2018, vol. 11071, pp. 12–20 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.College of StatisticsUniversity of International Business and EconomicsBeijingChina
  2. 2.Hudongfeng Technology (Beijing) Co., Ltd.BeijingChina

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