Extended Invariant Information Clustering Is Effective for Leave-One-Site-Out Cross-Validation in Resting State Functional Connectivity Modeling

Herein, we propose a new deep neural network model based on invariant information clustering (IIC), proposed by Ji et al., to improve the modeling performance of the leave-one-site-out cross-validation (LOSO-CV) for a multi-source dataset. Our Extended IIC (EIIC) is a type of contrastive learning; h...

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Detalles Bibliográficos
Autores principales: Okamoto, Naoki, Akama, Hiroyuki
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Neuroinformatics
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8671136/
https://www.ncbi.nlm.nih.gov/pubmed/34924987
http://dx.doi.org/10.3389/fninf.2021.709179

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8671136/
https://www.ncbi.nlm.nih.gov/pubmed/34924987
http://dx.doi.org/10.3389/fninf.2021.709179

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