@inproceedings{79e6a1fc27664d27bb69f3ff58c7e2f9,
title = "Transductive maximum margin classification of ADHD using resting state fMRI",
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.",
keywords = "ADHD classification, Maximum margin classification, Rs-fMRI, Transductive learning",
author = "Lei Wang and Danping Li and Tiancheng He and Wong, {Stephen T.C.} and Zhong Xue",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.; 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 ; Conference date: 17-10-2016 Through 17-10-2016",
year = "2016",
doi = "10.1007/978-3-319-47157-0_27",
language = "English (US)",
isbn = "9783319471563",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer-Verlag",
pages = "221--228",
editor = "Li Wang and Heung-Il Suk and Yinghuan Shi and Ehsan Adeli and Qian Wang",
booktitle = "Machine Learning in Medical Imaging - 7th International Workshop, MLMI 2016 held in conjunction with MICCAI 2016, Proceedings",
}