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Authors
Oh, Sewook
Kim, Sunghun
Kim, Jun Pyo
Seo, Sang Won
Park, Bo-yong https://orcid.org/0000-0001-7096-337X
Park, Hyunjin
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Funding
Funding for this research was provided by:
National Research Foundation grant funded by the Korea government (NRF-2020M3E5D2A01084892)
AI Graduate School Support Program (RS-2019-II190421)
National Research Foundation grant funded by the Korea government (RS-2024-00408040)
ICT Creative Consilience program (RS-2020-II201821)
Institute for Information and Communications Technology Planning and Evaluation (IITP) funded by the Korea Government (No.2022-0-00448/RS-2022-II220448, Deep Total Recall: Continual Learning for Human-Like Recall of Artificial Neural Networks, RS-2021-II212068, Artificial Intelligence Innovation Hub)
Sungkyunkwan University
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Article History
Received: 4 February 2025
Accepted: 2 May 2025
First Online: 15 May 2025
Declarations
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: Institutional Review Board (IRB) approvals were obtained from the original study depicting ADNI. In the ADNI dataset, consent forms were approved by each participating institution’s IRB. All ADNI data have been fully anonymized, with no protected health information included. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.
: The authors declare no competing interests.
