Abstract
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.
Fenrui Xiao and Ruifeng Kuang—The two authors contributed equally to this work.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Classification of Normal versus Malignant Cells in B-ALL White Blood Cancer Microscopic Images: ISBI 2019.
- 2.
Since the label of the final test set cannot be obtained, all the following experimental results are completed based on the preliminary test set.
References
Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images. Technical report, Citeseer (2009)
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)
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). https://doi.org/10.1109/ISBI.2016.7493362
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). https://doi.org/10.1109/ISBI.2017.7950542
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)
Yosinski, J., Clune, J., Nguyen, A., Fuchs, T., Lipson, H.: Understanding neural networks through deep visualization (2015). arXiv:1506.06579
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)
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)
Young, I.T.: The classification of white blood cells. IEEE Trans. Biomed. Eng. 4, 291–298 (1972)
Faraki, M., Harandi, M.T., Wiliem, A., Lovell, B.C.: Fisher tensors for classifying human epithelial cells. Pattern Recognit. 47(7), 2348–2359 (2014)
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)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P., et al.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
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)
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)
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)
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)
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)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014). arXiv:1409.1556
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)
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)
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)
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)
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)
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)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Xiao, F., Kuang, R., Ou, Z., Xiong, B. (2019). DeepMEN: Multi-model Ensemble Network for B-Lymphoblast Cell Classification. In: Gupta, A., Gupta, R. (eds) ISBI 2019 C-NMC Challenge: Classification in Cancer Cell Imaging. Lecture Notes in Bioengineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-0798-4_9
Download citation
DOI: https://doi.org/10.1007/978-981-15-0798-4_9
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-0797-7
Online ISBN: 978-981-15-0798-4
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)