Dynamic routing capsule networks for mild cognitive impairment diagnosis

Zhicheng Jiao, Pu Huang, Tae Eui Kam, Li Ming Hsu, Ye Wu, Han Zhang, Dinggang Shen

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

Abstract

Alzheimer’s disease (AD) is a chronic neurodegenerative disease that could cause severe cognitive damage to the patients. Diagnosis of AD at its preclinical stage, i.e., mild cognitive impairment (MCI), could help to prevent or slow down AD progression. With machine learning, automatic MCI diagnosis could be achieved. Most of the previous studies mainly share a similar frame-work, i.e., building a classifier based on the features extracted from static or dynamic functional connectivity. Recently, inspired by the great successes achieved by deep learning in other areas of medical image analysis, researchers have introduced neural network models for MCI diagnosis. In this paper, we propose dynamic routing capsule networks for MCI diagnosis. Our proposed methods are based on a novel neural network fashion of capsule net. Two variants of capsule net are designed and discussed, which respectively uses the intra-ROIs and inter-ROIs dynamic routing to obtain functional representation. More importantly, we design a learnable dynamic functional connectivity metric in our inter-ROIs dynamic model, in which the functional connectivity is dynamically learned during network training. To the best of our knowledge, it’s the first time to propose dynamic routing capsule networks for MCI diagnosis. Compared with other machine learning methods and deep learning model, our method can achieve superior performance from various aspects of evaluations.

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
Pages620-628
Number of pages9
ISBN (Print)9783030322502
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)
Volume11767 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

  • Alzheimer’s disease
  • Capsule networks
  • Computer-aided diagnosis
  • Deep learning
  • Mild cognitive impairment

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Jiao, Z., Huang, P., Kam, T. E., Hsu, L. M., Wu, Y., Zhang, H., & Shen, D. (2019). Dynamic routing capsule networks for mild cognitive impairment 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. 620-628). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11767 LNCS). Springer. https://doi.org/10.1007/978-3-030-32251-9_68