TY - GEN
T1 - Meta-modulation Network for Domain Generalization in Multi-site fMRI Classification
AU - Lee, Jaein
AU - Kang, Eunsong
AU - Jeon, Eunjin
AU - Suk, Heung Il
N1 - 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.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Autism spectrum disorder
KW - Domain generalization
KW - Meta-learning
KW - Modulation network
KW - Multi-site
KW - Resting-state functional magnetic resonance imaging
UR - http://www.scopus.com/inward/record.url?scp=85116445618&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-87240-3_48
DO - 10.1007/978-3-030-87240-3_48
M3 - Conference contribution
AN - SCOPUS:85116445618
SN - 9783030872397
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 500
EP - 509
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 - 24th International Conference, Proceedings
A2 - de Bruijne, Marleen
A2 - Cattin, Philippe C.
A2 - Cotin, Stéphane
A2 - Padoy, Nicolas
A2 - Speidel, Stefanie
A2 - Zheng, Yefeng
A2 - Essert, Caroline
PB - Springer Science and Business Media Deutschland GmbH
T2 - 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021
Y2 - 27 September 2021 through 1 October 2021
ER -