Interpretable Feature Learning Using Multi-output Takagi-Sugeno-Kang Fuzzy System for Multi-center ASD Diagnosis

Jun Wang, Ying Zhang, Tao Zhou, Zhaohong Deng, Huifang Huang, Shitong Wang, Jun Shi, Dinggang Shen

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

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 languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings
EditorsDinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou
PublisherSpringer
Pages790-798
Number of pages9
ISBN (Print)9783030322472
DOIs
Publication statusPublished - 2019 Jan 1
Externally publishedYes
Event22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, China
Duration: 2019 Oct 132019 Oct 17

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11766 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
CountryChina
CityShenzhen
Period19/10/1319/10/17

Keywords

  • Autism
  • Interpretable feature learning
  • Manifold regularization
  • Resting-state functional magnetic resonance imaging (rs-fMRI)

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Interpretable Feature Learning Using Multi-output Takagi-Sugeno-Kang Fuzzy System for Multi-center ASD Diagnosis'. Together they form a unique fingerprint.

  • Cite this

    Wang, J., Zhang, Y., Zhou, T., Deng, Z., Huang, H., Wang, S., Shi, J., & Shen, D. (2019). Interpretable Feature Learning Using Multi-output Takagi-Sugeno-Kang Fuzzy System for Multi-center ASD Diagnosis. In D. Shen, P-T. Yap, T. Liu, T. M. Peters, A. Khan, L. H. Staib, C. Essert, & S. Zhou (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings (pp. 790-798). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11766 LNCS). Springer. https://doi.org/10.1007/978-3-030-32248-9_88