Whole heart 3D+T representation learning through sparse 2D cardiac MR images

Zhang, Y. orcid.org/0009-0008-7725-6369, Chen, C. orcid.org/0000-0002-3525-9755, Shit, S. orcid.org/0000-0003-4435-7207 et al. (3 more authors) (2024) Whole heart 3D+T representation learning through sparse 2D cardiac MR images. In: Linguraru, M.G., Dou, Q., Feragen, A., Giannarou, S., Glocker, B., Lekadir, K. and Schnabel, J.A., (eds.) Medical Image Computing and Computer Assisted Intervention – MICCAI 2024. MICCAI 2024, 06-10 Oct 2024, Marrakesh, Morocco. Lecture Notes in Computer Science, 15001 . Springer Nature Switzerland , pp. 359-369. ISBN 9783031723773

Abstract

Metadata

Item Type: Proceedings Paper
Authors/Creators:
Editors:
  • Linguraru, M.G.
  • Dou, Q.
  • Feragen, A.
  • Giannarou, S.
  • Glocker, B.
  • Lekadir, K.
  • Schnabel, J.A.
Copyright, Publisher and Additional Information:

© 2024 The Authors. Except as otherwise noted, this author-accepted version of a paper published in Medical Image Computing and Computer Assisted Intervention – MICCAI 2024 is made available via the University of Sheffield Research Publications and Copyright Policy under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/

Keywords: Information and Computing Sciences; Biomedical Imaging; Heart Disease; Bioengineering; Networking and Information Technology R&D (NITRD); Cardiovascular; Evaluation of markers and technologies; Cardiovascular
Dates:
  • Published: 3 October 2024
  • Published (online): 2 October 2024
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: 14 Nov 2024 14:07
Last Modified: 14 Nov 2024 14:07
Status: Published
Publisher: Springer Nature Switzerland
Series Name: Lecture Notes in Computer Science
Refereed: Yes
Identification Number: 10.1007/978-3-031-72378-0_34
Related URLs:
Open Archives Initiative ID (OAI ID):

Export

Statistics