Abstract
Resting-state functional magnetic resonance imaging (rs-fMRI) provides key neural imaging characteristics for quantitative assessment and better understanding of the mechanisms of attention deficit hyperactivity disorder (ADHD). Recent multivariate analysis studies showed that functional connectivity (FC) could be used to classify ADHD from normal controls at the individual level. However, there may not be sufficient large numbers of labeled training samples for a hand-on classifier especially for disease classification. In this paper, we propose a transductive maximum margin classification (TMMC) method that uses the available unlabeled data in the learning process. On one hand, the maximum margin classification (MMC) criterion is used to maximize the class margin for the labeled data; on the other hand, a smoothness constraint is imposed on both labeled and unlabeled data projection so that similar samples tend to share the same label. To evaluate the performance of TMMC, experiments on a benchmark cohort from the ADHD-200 competition were performed. The results show that TMMC can improve the performance of ADHD classification using rs-fMRI by involving unlabeled samples, even for small number of labeled training data.
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Acknowledgements
This work was partially supported by National Natural Science Foundation of China (No. 61203137, 61401328), Natural Science Foundation of Shaanxi Province (No. 2014JQ8306, 2015JM6279), the Fundamental Research Funds for the Central Universities (No. K5051301007), and NIH 5-R03-EB018977 (ZX).
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Wang, L., Li, D., He, T., Wong, S.T.C., Xue, Z. (2016). Transductive Maximum Margin Classification of ADHD Using Resting State fMRI. 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_27
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DOI: https://doi.org/10.1007/978-3-319-47157-0_27
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