Improving Single-Modal Neuroimaging Based Diagnosis of Brain Disorders via Boosted Privileged Information Learning Framework
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.
KeywordsSupport Vector Machine Mild Cognitive Impairment Weak Classifier Multiple Kernel Multiple Kernel Learning
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.
- 6.Pechyony, D., Izmailov, R., Vashist, A., Vapnik, V.: SMO-style algorithms for learning using privileged information. In: DMIN, pp. 235–241 (2010)Google Scholar
- 9.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)Google Scholar