TY - GEN
T1 - Transductive maximum margin classification of ADHD using resting state fMRI
AU - Wang, Lei
AU - Li, Danping
AU - He, Tiancheng
AU - Wong, Stephen T.C.
AU - Xue, Zhong
N1 - Funding Information:
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).
Publisher Copyright:
© Springer International Publishing AG 2016.
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
KW - ADHD classification
KW - Maximum margin classification
KW - Rs-fMRI
KW - Transductive learning
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U2 - 10.1007/978-3-319-47157-0_27
DO - 10.1007/978-3-319-47157-0_27
M3 - Conference contribution
AN - SCOPUS:84992486761
SN - 9783319471563
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 221
EP - 228
BT - Machine Learning in Medical Imaging - 7th International Workshop, MLMI 2016 held in conjunction with MICCAI 2016, Proceedings
A2 - Wang, Li
A2 - Suk, Heung-Il
A2 - Shi, Yinghuan
A2 - Adeli, Ehsan
A2 - Wang, Qian
PB - Springer-Verlag
T2 - 7th International Workshop on Machine Learning in Medical Imaging, MLMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016
Y2 - 17 October 2016 through 17 October 2016
ER -