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Unregistered Bosniak Classification with Multi-phase Convolutional Neural Networks

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 9950)


Deep learning has been a growing trend in various fields of natural image classification as it performs state-of-the-art result on several challenging tasks. Despite its success, deep learning applied to medical image analysis has not been wholly explored. In this paper, we study on convolutional neural network (CNN) architectures applied to a Bosniak classification problem to classify Computed Tomography images into five Bosniak classes. We use a new medical image dataset called as the Bosniak classification dataset which will be fully introduced in this paper. For this data set, we employ a multi-phase CNN approach to predict classification accuracy. We also discuss the representation power of CNN compared to previously developed features (Garbor features) in medical image. In our experiment, we use data combination method to enlarge the data set to avoid overfitting problem in multi-phase medical imaging system. Using multi-phase CNN and data combination method we proposed, we have achieved 48.9 % accuracy on our test set, which improves the hand-crafted features by 11.9 %.


  • Medical image
  • Bosniak classification
  • Deep convolutional neural network
  • Unregistered medical image

This work was supported by Brain Fusion Program of Soul National University (800-20140265).

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Fig. 1.
Fig. 2.


  1. 1.

    Digital image and Communications in Medicine (DICOM) is a standard for handling, storing, printing, and transmitting information in medical image.

  2. 2.

    The term ‘unregistered’ is used to indicate that the three images from different phases have different shapes and sizes of lesions.


  1. 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 

  2. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition, arXiv preprint arXiv:1409.1556 (2014)

  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. Xu, Y., Mo, T., Feng, Q., Zhong, P., Lai, M., Chang, E.I., et al.: Deep learning of feature representation with multiple instance learning for medical image analysis. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1626–1630. IEEE (2014)

    Google Scholar 

  5. 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, Part II. LNCS, vol. 8150, pp. 411–418. Springer, Heidelberg (2013)

    CrossRef  Google Scholar 

  6. Miranda, C.M.N.R.D., Maranhão, C.P.D.M., Santos, C.J.J.D., Padilha, I.G., Farias, L.D.P.G.D., Rocha, M.S.D.: Bosniak classification of renal cystic lesions according to multidetector computed tomography findings. Radiol. Bras. 47(2), 115–121 (2014)

    CrossRef  Google Scholar 

  7. Lee, Y., Kim, N., Cho, K.-S., Kang, S.-H., Kim, D.Y., Jung, Y.Y., Kim, J.K.: Bayesian classifier for predicting malignant renal cysts on MDCT: early clinical experience. Am. J. Roentgenol. 193(2), W106–W111 (2009)

    CrossRef  Google Scholar 

  8. Curry, N.S., Cochran, S.T., Bissada, N.K.: Cystic renal masses: accurate Bosniak classification requires adequate renal CT. Am. J. Roentgenol. 175(2), 339–342 (2000)

    CrossRef  Google Scholar 

  9. Soares, J.V., Leandro, J.J., Cesar Jr., R.M., Jelinek, H.F., Cree, M.J.: Retinal vessel segmentation using the 2-D gabor wavelet and supervised classification. IEEE Trans. Med. Imaging 25(9), 1214–1222 (2006)

    Google Scholar 

  10. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929–1958 (2014)

    MathSciNet  MATH  Google Scholar 

  11. Farabet, C., Couprie, C., Najman, L., LeCun, Y.: Learning hierarchical features for scene labeling. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1915–1929 (2013)

    CrossRef  Google Scholar 

  12. Lin, M., Chen, Q., Yan, S.: Network in network, arXiv preprint arXiv:1312.4400 (2013)

  13. Kyrki, V., Kamarainen, J.-K., Kälviäinen, H.: Simple gabor feature space for invariant object recognition. Pattern Recognit. Lett. 25(3), 311–318 (2004)

    CrossRef  Google Scholar 

  14. Yang, M., Zhang, L.: Gabor feature based sparse representation for face recognition with gabor occlusion dictionary. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part VI. LNCS, vol. 6316, pp. 448–461. Springer, Heidelberg (2010)

    CrossRef  Google Scholar 

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Correspondence to Nojun Kwak .

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Lee, M., Lee, H., Oh, J., Lee, H.J., Kim, S.H., Kwak, N. (2016). Unregistered Bosniak Classification with Multi-phase Convolutional Neural Networks. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9950. Springer, Cham.

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