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Deep Neural Network with l2-Norm Unit for Brain Lesions Detection

  • Mina Rezaei
  • Haojin Yang
  • Christoph Meinel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10637)

Abstract

Automated brain lesions detection is an important and very challenging clinical diagnostic task, because the lesions have different sizes, shapes, contrasts and locations. Deep Learning recently shown promising progresses in many application fields, which motivates us to apply this technology for such important problem. In this paper we propose a novel and end-to-end trainable approach for brain lesions classification and detection by using deep Convolutional Neural Network (CNN). In order to investigate the applicability, we applied our approach on several brain diseases including high and low grade glioma tumor, ischemic stroke, Alzheimer diseases, by which the brain Magnetic Resonance Images (MRI) have been applied as input for the analysis. We proposed a new operation unit which receives features from several projections of a subset units of the bottom layer and computes a normalized l2-norm for next layer. We evaluated the proposed approach on two different CNN architectures and number of popular benchmark datasets. The experimental results demonstrate the superior ability of the proposed approach.

Keywords

Multimodal CNN l2-norm unit Brain lesion detection and localization 

References

  1. 1.
    Menze, B., Reyes, M., Van Leemput, K.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34, 1993–2024 (2014)CrossRefGoogle Scholar
  2. 2.
    LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)CrossRefGoogle Scholar
  3. 3.
    Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)Google Scholar
  4. 4.
    Gulcehre, C., Cho, K., Pascanu, R., Bengio, Y.: Learned-norm pooling for deep feedforward and recurrent neural networks. In: Calders, T., Esposito, F., Hüllermeier, E., Meo, R. (eds.) ECML PKDD 2014. LNCS, vol. 8724, pp. 530–546. Springer, Heidelberg (2014). doi: 10.1007/978-3-662-44848-9_34 Google Scholar
  5. 5.
    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)Google Scholar
  6. 6.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)Google Scholar
  7. 7.
    Szegedy, C., Toshev, A., Erhan, D.: Deep neural networks for object detection. In: Advances in Neural Information Processing Systems, pp. 2553–2561 (2013)Google Scholar
  8. 8.
    Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014)Google Scholar
  9. 9.
    Ren, S., He, K., Girshick, R.B., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. CoRR abs/1506.01497 (2015)Google Scholar
  10. 10.
    Paul, J.S., Plassard, A.J., Landman, B.A., Fabbri, D.: Deep learning for brain tumor classification, vol. 10137, pp. 1013710-1–1013710-16 (2017)Google Scholar
  11. 11.
    El Abbadi, N.K., Kadhim, N.E.: Brain cancer classification based on features and artificial neural network. Brain 6(1) (2017)Google Scholar
  12. 12.
    Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). doi: 10.1007/978-3-319-24574-4_28 CrossRefGoogle Scholar
  13. 13.
    Dai, J., He, K., Sun, J.: Instance-aware semantic segmentation via multi-task network cascades. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)Google Scholar
  14. 14.
    Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R.B., Guadarrama, S., Darrell, T.: Caffe: convolutional architecture for fast feature embedding. CoRRGoogle Scholar
  15. 15.
    Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. CoRR (2015)Google Scholar
  16. 16.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
  17. 17.
    Girshick, R.B.: Fast R-CNN. CoRR abs/1504.08083 (2015)Google Scholar
  18. 18.
    Uijlings, J., van de Sande, K., Gevers, T., Smeulders, A.: Selective search for object recognition. Int. J. Comput. Vis. 104, 154–171 (2013)CrossRefGoogle Scholar
  19. 19.
    Liu, G., Zhang, X., Zhou, S.: Multi-class classification of support vector machines based on double binary tree. In: Fourth International Conference on Natural Computation, ICNC 2008, vol. 2, pp. 102–105. IEEE (2008)Google Scholar
  20. 20.
  21. 21.
  22. 22.
  23. 23.
  24. 24.
    Havaei, M., Davy, A., Warde-Farley, D., Biard, A., Courville, A., Bengio, Y., Pal, C., Jodoin, P.M., Larochelle, H.: Brain tumor segmentation with deep neural networks. Med. Image Anal. 35, 18–31 (2017)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Hasso Plattner InstitutePotsdamGermany

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