Abstract
Ultrasound imaging is a most common modality for tumor detection and diagnosis. Deep learning (DL) algorithms generally suffer from the small sample problem. The traditional texture feature extraction methods are still commonly used for small ultrasound image dataset. Deep polynomial network (DPN) is a newly proposed DL algorithm with excellent feature representation, which has the potential for small dataset. However, the simple concatenation of the learned hierarchical features from different layers in DPN limits its performance. Since the features from different layers in DPN can be regarded as heterogeneous features, they then can be effectively integrated by multiple kernel learning (MKL) methods. In this work, we propose a DPN and MKL based feature learning and classification framework (DPN-MKL) for tumor classification on small ultrasound image dataset. The experimental results show that DPN-MKL algorithm outperforms the commonly used DL algorithms for ultrasound image based tumor classification on small dataset.
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References
Prasoon, A., Petersen, K., Igel, C., Lauze, F., Dam, E., Nielsen, M.: Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013, Part II. LNCS, vol. 8150, pp. 246–253. Springer, Heidelberg (2013)
Shin, H.C., Orton, M.R., Collins, D.J., Doran, S.J., Leach, M.O.: Stacked autoencoders for unsupervised feature learning and multiple organ detection in a pilot study using 4D patient data. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1930–1943 (2013)
Roth, H.R., Lu, L., Seff, A., Cherry, K.M., Hoffman, J., Wang, S., Liu, J., Turkbey, E., Summers, R.M.: A new 2.5D representation for lymph node detection using random sets of deep convolutional neural network observations. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014, Part I. LNCS, vol. 8673, pp. 520–527. Springer, Heidelberg (2014)
Carneiro, G., Nascimento, J.C.: Combining multiple dynamic models and deep learning architectures for tracking the left ventricle endocardium in ultrasound data. IEEE Trans. Pattern Anal. Mach. Intell. 35(11), 2592–2607 (2013)
Sehgal, C.M., Weinstein, S.P., Arger, P.H., Conant, E.F.: A review of breast ultrasound. J. Mammary Gland Biol. 11(2), 113–123 (2006)
Livni, R., Shalev-Shwartz, S., Shamir, O.: An algorithm for training polynomial networks. arXiv: 1304.7045 (2014)
Xu, C., Tao, D., Xu, C.: A aurvey on multi-view learning. arXiv: 1304.5634 (2013)
Rakotomamonjy, A., Bach, F.R., Canu, S., Grandvalet, Y.: SimpleMKL. J. Mach. Learn. Res. 9, 2491–2521 (2008)
Xu, X.X., Tsang, I.W., Xu, D.: Soft Margin Multiple Kernel Learning. IEEE Trans. Neural Network Learn. Sys. 24(5), 749–761 (2013)
Easley, G., Labate, D., Lim, W.Q.: Sparse directional image representations using the discrete shearlet transform. Appl. Comput. Harmon. Anal. 25, 25–46 (2008)
Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new rerspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798–1828 (2013)
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© 2015 Springer International Publishing Switzerland
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Liu, X., Shi, J., Zhang, Q. (2015). Tumor Classification by Deep Polynomial Network and Multiple Kernel Learning on Small Ultrasound Image Dataset. In: Zhou, L., Wang, L., Wang, Q., Shi, Y. (eds) Machine Learning in Medical Imaging. MLMI 2015. Lecture Notes in Computer Science(), vol 9352. Springer, Cham. https://doi.org/10.1007/978-3-319-24888-2_38
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DOI: https://doi.org/10.1007/978-3-319-24888-2_38
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