A multi-classification accessment framework for reproducible evaluation of multimodal learning in Alzheimer's disease

Nan, F., Li, S., Wang, J. et al. (6 more authors) (2022) A multi-classification accessment framework for reproducible evaluation of multimodal learning in Alzheimer's disease. IEEE/ACM Transactions on Computational Biology and Bioinformatics. ISSN 1545-5963

Abstract

Metadata

Authors/Creators:
  • Nan, F.
  • Li, S.
  • Wang, J.
  • Tang, Y.
  • Jun, Q.
  • Zhou, M.
  • Zhao, Z.
  • Yang, Y.
  • Yang, P.
Copyright, Publisher and Additional Information: © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy.
Keywords: Multi-modal learning; Multi-modality data; Alzheimer's disease
Dates:
  • Accepted: 27 August 2022
  • Published (online): 6 September 2022
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield)
Depositing User: Symplectic Sheffield
Date Deposited: 15 Sep 2022 13:13
Last Modified: 15 Sep 2022 14:25
Status: Published online
Publisher: Institute of Electrical and Electronics Engineers
Refereed: Yes
Identification Number: https://doi.org/10.1109/TCBB.2022.3204619

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