Abstract
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.
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- 1.
CALTECH, KKI, LEUVEN, MAX_MUN, NYU, OHSU, OLIN, PITT, SBL, SDSU, STANFORD, TRINITY, UCLA, UM, USM, YALE.
- 2.
Scan procedure and parameters can be found at http://fcon_1000.projects.nitrc.org/indi/abide/.
- 3.
Note that we excluded CMU site in ABIDE because only ASD subjects left after removing subjects with pre-processing problem.
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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)).
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Lee, J., Kang, E., Jeon, E., Suk, HI. (2021). Meta-modulation Network for Domain Generalization in Multi-site fMRI Classification. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12905. Springer, Cham. https://doi.org/10.1007/978-3-030-87240-3_48
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