Enriched Representation Learning in Resting-State fMRI for Early MCI Diagnosis

Eunjin Jeon, Eunsong Kang, Jiyeon Lee, Jaein Lee, Tae Eui Kam, Heung Il Suk

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

Abstract

In recent studies, we have witnessed the applicability of deep learning methods on resting-state functional Magnetic Resonance Image (rs-fMRI) analysis and on its use for brain disease diagnosis, e.g., early Mild Cognitive Impairment (eMCI) identification. However, to our best knowledge, many of the existing methods are generally limited from improving the performance in a target task, e.g., eMCI diagnosis, by the unexpected information loss in transforming an input into hierarchical or compressed representations. In this paper, we propose a novel network architecture that discovers enriched representations of the spatio-temporal patterns in rs-fMRI such that the most compressed or latent representations include the maximal amount of information to recover the original input, but are decomposed into diagnosis-relevant and diagnosis-irrelevant features. In order to learn those favourable representations, we utilize a self-attention mechanism to explore spatially more informative patterns over time and information-oriented techniques to maintain the enriched but decomposed representations. In our experiments over the ADNI dataset, we validated the effectiveness of the proposed network architecture by comparing its performance with that of the counterpart methods as well as the competing methods in the literature.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings
EditorsAnne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz
PublisherSpringer Science and Business Media Deutschland GmbH
Pages397-406
Number of pages10
ISBN (Print)9783030597276
DOIs
Publication statusPublished - 2020
Event23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 - Lima, Peru
Duration: 2020 Oct 42020 Oct 8

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12267 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020
CountryPeru
CityLima
Period20/10/420/10/8

Keywords

  • Brain disease diagnosis
  • Deep learning
  • Early Mild Cognitive Impairment
  • Mutual information
  • Resting-state functional magnetic resonance imaging
  • Self-attention

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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