Prostate Motion Modelling Using Biomechanically-Trained Deep Neural Networks on Unstructured Nodes

Saeed, SU, Taylor, ZA orcid.org/0000-0002-0718-1663, Pinnock, MA et al. (3 more authors) (2020) Prostate Motion Modelling Using Biomechanically-Trained Deep Neural Networks on Unstructured Nodes. In: Lecture Notes in Computer Science. MICCAI 2020: Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 04-08 Oct 2020, Lima, Peru. Springer Verlag , pp. 650-659. ISBN 9783030597184

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Copyright, Publisher and Additional Information: © Springer Nature Switzerland AG 2020. This is an author produced version of a conference paper published in Lecture Notes for Computer Science. Uploaded in accordance with the publisher's self-archiving policy.
Keywords: Deep learning; Biomechanical modelling; PointNet
Dates:
  • Published (online): 29 September 2020
  • Published: 29 September 2020
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mechanical Engineering (Leeds) > Institute of Medical and Biological Engineering (iMBE) (Leeds)
Funding Information:
FunderGrant number
Cancer Research UKA28832
Depositing User: Symplectic Publications
Date Deposited: 25 Nov 2020 13:32
Last Modified: 25 Nov 2020 13:32
Status: Published
Publisher: Springer Verlag
Identification Number: https://doi.org/10.1007/978-3-030-59719-1_63

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