Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental disorder closely related to potential dysfunction of the brain. Although multiple functional connectivity (FC) features such as low-order functional connectivity (LOFC) and high-order functional connectivity (HOFC) provide complementary knowledge to each other, it is still challenging to find interpretable LOFC and HOFC features for multi-center ASD diagnosis. To this end, we develop a novel interpretable feature learning method based on multi-output TSK fuzzy system (MO-TSK-FS) for multi-center ASD diagnosis. Specifically, both the LOFC and HOFC features are first mapped to a high-dimensional space using the premise part of MO-TSK-FS, which shares the common knowledge across multiple centers. Then, the mapped features are transformed to a low-dimensional feature space using a transformation matrix. A novel unsupervised learning problem is formulated to find the optimal transformation matrix. Finally, a multi-modality support vector classifier (M2SVC) is constructed for classification. The experimental results show that the proposed interpretable feature learning method for multi-center ASD classification can effectively extract important features from the original LOFC and HOFC features, resulting in an efficient M2SVC for multi-center ASD classification.
Original language | English |
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Title of host publication | Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings |
Editors | Dinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou |
Publisher | Springer Science and Business Media Deutschland GmbH |
Pages | 790-798 |
Number of pages | 9 |
ISBN (Print) | 9783030322472 |
DOIs | |
Publication status | Published - 2019 |
Externally published | Yes |
Event | 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, China Duration: 2019 Oct 13 → 2019 Oct 17 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 11766 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 |
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Country/Territory | China |
City | Shenzhen |
Period | 19/10/13 → 19/10/17 |
Bibliographical note
Funding Information:Acknowledgements.. This work was supported in part by the Natural Science Foundation of Jiangsu Province (BK20151299, BK20151358, BK20161268 and BK20181339).
Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
Keywords
- Autism
- Interpretable feature learning
- Manifold regularization
- Resting-state functional magnetic resonance imaging (rs-fMRI)
ASJC Scopus subject areas
- General Computer Science
- Theoretical Computer Science