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Detecting 10,000 Cells in One Second

  • Zheng Xu
  • Junzhou HuangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9901)

Abstract

In this paper, we present a generalized distributed deep neural network architecture to detect cells in whole-slide high-resolution histopathological images, which usually hold \(10^{8}\) to \(10^{10}\) pixels. Our framework can adapt and accelerate any deep convolutional neural network pixel-wise cell detector to perform whole-slide cell detection within a reasonable time limit. We accelerate the convolutional neural network forwarding through a sparse kernel technique, eliminating almost all of the redundant computation among connected patches. Since the disk I/O becomes a bottleneck when the image size scale grows larger, we propose an asynchronous prefetching technique to diminish a large portion of the disk I/O time. An unbalanced distributed sampling strategy is proposed to enhance the scalability and communication efficiency in distributed computing. Blending advantages of the sparse kernel, asynchronous prefetching and distributed sampling techniques, our framework is able to accelerate the conventional convolutional deep learning method by nearly 10, 000 times with same accuracy. Specifically, our method detects cells in a \(10^{8}\)-pixel (\(10^4\times 10^4\)) image in 20  s (approximately 10, 000 cells per second) on a single workstation, which is an encouraging result in whole-slide imaging practice.

Keywords

Convolutional Neural Network Cell Detection Original Network Deep Neural Network Histopathological Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

The authors would like to thank NVIDIA for GPU donation and the National Cancer Institute for access to NCI’s data collected by the National Lung Screening Trial. The statements contained herein are solely of the authors and do not represent or imply concurrence or endorsement by NCI.

References

  1. 1.
    Arteta, C., Lempitsky, V., Noble, J.A., Zisserman, A.: Learning to detect cells using non-overlapping extremal regions. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012. LNCS, vol. 7510, pp. 348–356. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-33415-3_43 CrossRefGoogle Scholar
  2. 2.
    Cireşan, D.C., Giusti, A., Gambardella, L.M., Schmidhuber, J.: Mitosis detection in breast cancer histology images with deep neural networks. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8150, pp. 411–418. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-40763-5_51 CrossRefGoogle Scholar
  3. 3.
    Xie, Y., Xing, F., Kong, X., Su, H., Yang, L.: Beyond classification: structured regression for robust cell detection using convolutional neural network. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 358–365. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-24574-4_43 CrossRefGoogle Scholar
  4. 4.
    Pan, H., Xu, Z., Huang, J.: An effective approach for robust lung cancer cell detection. In: Wu, G., Coupé, P., Zhan, Y., Munsell, B., Rueckert, D. (eds.) Patch-MI 2015. LNCS, vol. 9467, pp. 87–94. Springer, Heidelberg (2015)Google Scholar
  5. 5.
    Giusti, A., Cireşan, D.C., Masci, J., Gambardella, L.M., Schmidhuber, J.: Fast image scanning with deep max-pooling convolutional neural networks. arXiv preprint arXiv:1302.1700 (2013)
  6. 6.
    Li, H., Zhao, R., Wang, X.: Highly efficient forward and backward propagation of convolutional neural networks for pixelwise classification. arXiv preprint arXiv:1412.4526 (2014)
  7. 7.
    Mareček, J., Richtárik, P., Takáč, M.: Distributed block coordinate descent for minimizing partially separable functions. In: Numerical Analysis and Optimization, pp. 261–288. Springer, Switzerland (2015)Google Scholar
  8. 8.
    Xu, Z., Huang, J.: Efficient lung cancer cell detection with deep convolution neural network. In: Wu, G., Coupé, P., Zhan, Y., Munsell, B., Rueckert, D. (eds.) Patch-MI 2015. LNCS, vol. 9467, pp. 79–86. Springer, Heidelberg (2015)Google Scholar
  9. 9.
    LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)CrossRefGoogle Scholar
  10. 10.
    Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: Convolutional architecture for fast feature embedding. arXiv preprint arXiv:1408.5093 (2014)
  11. 11.
    Network, C.G.A.R., et al.: Comprehensive molecular profiling of lung adenocarcinoma. Nature 511(7511), 543–550 (2014)CrossRefGoogle Scholar
  12. 12.
    Yao, J., Ganti, D., Luo, X., Xiao, G., Xie, Y., Yan, S., Huang, J.: Computer-assisted diagnosis of lung cancer using quantitative topology features. In: Zhou, L., Wang, L., Wang, Q., Shi, Y. (eds.) MLMI 2015. LNCS, vol. 9352, pp. 288–295. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-24888-2_35 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringUniversity of Texas at ArlingtonArlingtonUSA

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