A probabilistic deep motion model for unsupervised cardiac shape anomaly assessment

Zakeri, A, Hokmabadi, A, Ravikumar, N et al. (2 more authors) (2022) A probabilistic deep motion model for unsupervised cardiac shape anomaly assessment. Medical Image Analysis, 75. 102276. ISSN 1361-8415

Abstract

Metadata

Item Type: Article
Authors/Creators:
Keywords: Cardiac shape quality control; Deep learning; Expectation-maximisation; Probabilistic model; Spatio-temporal anomaly detection; UK Biobank
Dates:
  • Published: January 2022
  • Published (online): 16 October 2021
  • Accepted: 15 October 2021
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds)
Funding Information:
Funder
Grant number
EPSRC (Engineering and Physical Sciences Research Council)
EP/S012796/1
Depositing User: Symplectic Publications
Date Deposited: 10 Dec 2021 12:13
Last Modified: 17 Dec 2021 14:50
Status: Published
Publisher: Elsevier
Identification Number: 10.1016/j.media.2021.102276
Related URLs:
Open Archives Initiative ID (OAI ID):

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