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한빛사논문

조회335

이재인Jaein Lee

고려대학교

IEEE Trans. Neural Netw. Learn. Syst., Sep 14 2023, PP | https://doi.org/10.1109/TNNLS.2023.3311195 Site-Invariant Meta-Modulation Learning for Multisite Autism Spectrum Disorders Diagnosis.

Jaein Lee1 , Eunsong Kang1, Da-Woon Heo , and Heung-Il Suk , Senior Member, IEEE

1Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea

2Department of Artificial Intelligence, Korea University, Seoul 02841, Republic of Korea

3Department of Artificial Intelligence and the Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea

Corresponding author: Heung-Il Suk

Abstract

Large amounts of fMRI data are essential to building generalized predictive models for brain disease diagnosis. In order to conduct extensive data analysis, it is often necessary to gather data from multiple organizations. However, the site variation inherent in multisite resting-state functional magnetic resonance imaging (rs-fMRI) leads to unfavorable heterogeneity in data distribution, negatively impacting the identification of biomarkers and the diagnostic decision. Several existing methods have alleviated this shift of domain distribution (i.e., multisite problem). Statistical tuning schemes directly regress out site disparity factors from the data prior to model training. Such methods have a limitation in processing data each time through variance estimation according to the added site. In the model adjustment approaches, domain adaptation (DA) methods adjust the features or models of the source domain according to the target domain during model training. Thus, it is inevitable that it needs updating model parameters according to the samples of a target site, causing great limitations in practical applicability. Meanwhile, the approach of domain generalization (DG) aims to create a universal model that can be quickly adapted to multiple domains. In this study, we propose a novel framework for disease diagnosis that alleviates the multisite problem by adaptively calibrating site-specific features into site-invariant features. Specifically, it applies directly to samples from unseen sites without the need for fine-tuning. With a learning-to-learn strategy that learns how to calibrate the features under the various domain shift environments, our novel modulation mechanism extracts site-invariant features. In our experiments over the Autism Brain Imaging Data Exchange (ABIDE I and II) dataset, we validated the generalization ability of the proposed network by improving diagnostic accuracy in both seen and unseen multisite samples.

논문정보

형식Research article
게재일2023년 09월 (BRIC 등록일 2023-10-06)
연구진국내 연구진
분야 바이오·의료융합 > 뇌인지과학

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