Meta-modulation Network for Domain Generalization in Multi-site fMRI Classification

Jaein Lee, Eunsong Kang, Eunjin Jeon, Heung Il Suk

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

7 Citations (Scopus)


In general, it is expected that large amounts of functional magnetic resonance imaging (fMRI) would be helpful to deduce statistically meaningful biomarkers or to build generalized predictive models for brain disease diagnosis. However, the site-variation inherent in rs-fMRI hampers the researchers to use the entire samples collected from multiple sites because it involves the unfavorable heterogeneity in data distribution, thus negatively impact on identifying biomarkers and making a diagnostic decision. To alleviate this challenging multi-site problem, we propose a novel framework that adaptively calibrates the site-specific features into site-invariant features via a novel modulation mechanism. Specifically, we take a learning-to-learn strategy and devise a novel meta-learning model for domain generalization, i.e., applicable to samples from unseen sites without retraining or fine-tuning. In our experiments over the ABIDE dataset, we validated the generalization ability of the proposed network by showing improved diagnostic accuracy in both seen and unseen multi-site samples.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2021 - 24th International Conference, Proceedings
EditorsMarleen de Bruijne, Philippe C. Cattin, Stéphane Cotin, Nicolas Padoy, Stefanie Speidel, Yefeng Zheng, Caroline Essert
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages10
ISBN (Print)9783030872397
Publication statusPublished - 2021
Event24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021 - Virtual, Online
Duration: 2021 Sept 272021 Oct 1

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12905 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021
CityVirtual, Online

Bibliographical note

Funding Information:
Acknowledgement. This work was supported by National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2019R1A2C1006543) and partially by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2019-0-00079, Artificial Intelligence Graduate School Program (Korea University)).

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.


  • Autism spectrum disorder
  • Domain generalization
  • Meta-learning
  • Modulation network
  • Multi-site
  • Resting-state functional magnetic resonance imaging

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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