Sparse multi-view task-centralized learning for ASD diagnosis

Jun Wang, Qian Wang, Shitong Wang, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

It is challenging to derive early diagnosis from neuroimaging data for autism spectrum disorder (ASD). In this work, we propose a novel sparse multi-view task-centralized (Sparse-MVTC) classification method for computer-assisted diagnosis of ASD. In particular, since ASD is known to be age- and sex-related, we partition all subjects into different groups of age/sex, each of which can be treated as a classification task to learn. Meanwhile, we extract multi-view features from functional magnetic resonance imaging to describe the brain connectivity of each subject. This formulates a multi-view multi-task sparse learning problem and it is solved by a novel Sparse-MVTC method. Specifically, we treat each task as a central task and other tasks as the auxiliary ones. We then consider the task-task and view-view relations between the central task and each auxiliary task. We can use this task-centralized strategy for a highly efficient solution. The comprehensive experiments on the ABIDE database demonstrate that our proposed Sparse-MVTC method can significantly outperform the existing classification methods in ASD diagnosis.

Original languageEnglish
Title of host publicationMachine Learning in Medical Imaging - 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Proceedings
PublisherSpringer Verlag
Pages159-167
Number of pages9
Volume10541 LNCS
ISBN (Print)9783319673882
DOIs
Publication statusPublished - 2017
Event8th International Workshop on Machine Learning in Medical Imaging, MLMI 2017 held in conjunction with the 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017 - Quebec City, Canada
Duration: 2017 Sep 102017 Sep 10

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10541 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other8th International Workshop on Machine Learning in Medical Imaging, MLMI 2017 held in conjunction with the 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017
CountryCanada
CityQuebec City
Period17/9/1017/9/10

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Wang, J., Wang, Q., Wang, S., & Shen, D. (2017). Sparse multi-view task-centralized learning for ASD diagnosis. In Machine Learning in Medical Imaging - 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Proceedings (Vol. 10541 LNCS, pp. 159-167). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10541 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-67389-9_19