Abstract
In clinical practice, it is more prevalent to use only a single-modal neuroimaging for diagnosis of brain disorders, such as structural magnetic resonance imaging. A neuroimaging dataset generally suffers from the small-sample-size problem, which makes it difficult to train a robust and effective classifier. The learning using privileged information (LUPI) is a newly proposed paradigm, in which the privileged information is available only at the training phase to provide additional information about training samples, but unavailable in the testing phase. LUPI can effectively help construct a better predictive rule to promote classification performance. In this paper, we propose to apply LUPI for the single-modal neuroimaging based diagnosis of brain diseases along with multi-modal training data. Moreover, a boosted LUPI framework is developed, which performs LUPI-based random subspace learning and then ensembles all the LUPI classifiers with the multiple kernel boosting (MKB) algorithm. The experimental results on two neuroimaging datasets show that LUPI-based algorithms are superior to the traditional classifier models for single-modal neuroimaging based diagnosis of brain disorders, and the proposed boosted LUPI framework achieves best performance.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zhang, D.Q., Wang, Y.P., Zhou, L.P., Yuan, H., Shen, D.G.: ADNI: multimodal classification of Alzheimer’s disease and mild cognitive impairment. NeuroImage 55(3), 856–867 (2011)
Mwangi, B., Tian, T.S., Soares, J.C.: A review of feature reduction techniques in neuroimaging. Neuroinformatics 12(2), 229–244 (2014)
Filipovych, R., Davatzikos, C.: Semi-supervised pattern classification of medical images: application to mild cognitive impairment (MCI). NeuroImage 55(3), 1109–1119 (2011)
Cheng, B., Liu, M.X., Zhang, D.Q., Munsell, B.C., Shen, D.G.: ADNI: domain transfer learning for MCI conversion prediction. IEEE Trans. Biomed. Eng. 62(7), 1805–1817 (2015)
Vapnik, V., Vashist, A.: A new learning paradigm: learning using privileged information. Neural Netw. 22, 544–557 (2009)
Pechyony, D., Izmailov, R., Vashist, A., Vapnik, V.: SMO-style algorithms for learning using privileged information. In: DMIN, pp. 235–241 (2010)
Kuncheva, L.I., RodrÃguez, J.J., Plumpton, C.O., Linden, D.E.J., Johnston, S.J.: Random subspace ensembles for fMRI classification. IEEE Trans. Med. Imaging 29(2), 531–542 (2010)
Yang, F., Lu, H.C., Yang, M.H.: Robust visual tracking via multiple kernel boosting with affinity constraints. IEEE Trans. Circuits Syst. Video Technol. 24(2), 242–254 (2014)
Silva, R.F., Castro, E., Gupta, C.N., Cetin, M., Arbabshirani, M., Potluru, V.K., Plis, S.M., Calhoun, V.D.: The tenth annual MLSP competition schizophrenia classification challenge. In: MLSP, pp. 1–6 (2014)
Jack, C.R., Bernstein, M.A., Fox, N.C., et al.: The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods. J. Magn. Reson. Imaging 27, 685–691 (2008)
Li, W., Duan, L.X., Xu, D., Tsang, I.W.: Learning with augmented features for supervised and semi-supervised heterogeneous domain adaptation. IEEE Trans. Pattern Anal. Mach. Intell. 36(6), 1134–1148 (2014)
Acknowledgments
This work is supported by the National Natural Science Foundation of China (61471231, 61401267, 11471208, 61201042, 61471245, U1201256), the Projects of Guangdong R/D Foundation and the New Technology R/D projects of Shenzhen City.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Zheng, X., Shi, J., Ying, S., Zhang, Q., Li, Y. (2016). Improving Single-Modal Neuroimaging Based Diagnosis of Brain Disorders via Boosted Privileged Information Learning Framework. In: Wang, L., Adeli, E., Wang, Q., Shi, Y., Suk, HI. (eds) Machine Learning in Medical Imaging. MLMI 2016. Lecture Notes in Computer Science(), vol 10019. Springer, Cham. https://doi.org/10.1007/978-3-319-47157-0_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-47157-0_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-47156-3
Online ISBN: 978-3-319-47157-0
eBook Packages: Computer ScienceComputer Science (R0)