Abstract
This paper presents a general discriminative dimensionality reduction framework for multi-modal image-based classification in medical imaging datasets. The major goal is to use all modalities simultaneously to transform very high dimensional image to a lower dimensional representation in a discriminative way. In addition to being discriminative, the proposed approach has the advantage of being clinically interpretable. We propose a framework based on regularized tensor decomposition. We show that different variants of tensor factorization imply various hypothesis about data. Inspired by the idea of multi-view dimensionality reduction in machine learning community, two different kinds of tensor decomposition and their implications are presented. We have validated our method on a multi-modal longitudinal brain imaging study. We compared this method with a publically available classification software based on SVM that has shown state-of-the-art classification rate in number of publications.
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Ando, R.K., Zhang, T.: Two-view feature generation model for semi-supervised learning. In: Proceedings of the 24th International Conference on Machine Learning, ICML 2007, pp. 25–32. ACM, New York (2007)
Batmanghelich, N., Taskar, B., Davatzikos, C.: A general and unifying framework for feature construction, in image-based pattern classification. Inf. Process. Med. Imaging 21, 423–434 (2009)
Batmanghelich, N., Ye, D.H., Pohl, K., Taskar, B., Davatzikos, C.: Disease classification and prediction via semi-supervised dimensionality reduction. In: 2011 IEEE International Symposium on Biomedical Imaging (2011)
Delis, D., Kramer, J., Kaplan, E., Ober, B.: California Verbal Learning Test-Research Edition. The Psychological Corporation, New York (1987)
Diehl, J., Grimmer, T., Drzezga, A., Riemenschneider, M., Frstl, H., Kurz, A.: Cerebral metabolic patterns at early stages of frontotemporal dementia and semantic dementia. a pet study. Neurobiol. Aging 25(8), 1051–1056 (2004)
Fan, Y., Shen, D., Gur, R.C., Gur, R.E., Davatzikos, C.: Compare: classification of morphological patterns using adaptive regional elements. IEEE Trans. Med. Imaging 26(1), 93–105 (2007)
Foster, N.L., Heidebrink, J.L., Clark, C.M., Jagust, W.J., Arnold, S.E., Barbas, N.R., DeCarli, C.S., Turner, R.S., Koeppe, R.A., Higdon, R., Minoshima, S.: Fdg-pet improves accuracy in distinguishing frontotemporal dementia and alzheimer’s disease. Brain 130(Pt 10), 2616–2635 (2007)
Fox, N.C., Schott, J.M.: Imaging cerebral atrophy: normal ageing to alzheimer’s disease. Lancet 363(9406), 392–394 (2004)
Golland, P., Grimson, W.E.L., Shenton, M.E., Kikinis, R.: Deformation analysis for shape based classification. In: Insana, M.F., Leahy, R.M. (eds.) IPMI 2001. LNCS, vol. 2082, pp. 517–530. Springer, Heidelberg (2001)
Hinrichs, C., Singh, V., Xu, G., Johnson, S.: Mkl for robust multi-modality ad classification. Med. Image Comput. Comput. Assist. Interv. 12(Pt 2), 786–794 (2009)
Kakade, S., Foster, D.: Multi-view regression via canonical correlation analysis, pp. 82–96 (2007)
Landau, S.M., Harvey, D., Madison, C.M., Reiman, E.M., Foster, N.L., Aisen, P.S., Petersen, R.C., Shaw, L.M., Trojanowski, J.Q., Jack, C.R., Weiner, M.W., Jagust, W.J., Initiative, A.D.N.: Comparing predictors of conversion and decline in mild cognitive impairment. Neurology 75(3), 230–238 (2010)
Shen, D., Davatzikos, C.: Very high-resolution morphometry using mass-preserving deformations and hammer elastic registration. Neuroimage 18(1), 28–41 (2003)
Zhang, D., Wang, Y., Zhou, L., Yuan, H., Shen, D.: The ADNI: Multimodal classification of alzheimer’s disease and mild cognitive impairment. Neuroimage (January 2011)
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Batmanghelich, N., Dong, A., Taskar, B., Davatzikos, C. (2011). Regularized Tensor Factorization for Multi-Modality Medical Image Classification. In: Fichtinger, G., Martel, A., Peters, T. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2011. MICCAI 2011. Lecture Notes in Computer Science, vol 6893. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23626-6_3
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DOI: https://doi.org/10.1007/978-3-642-23626-6_3
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