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di Folco, M. ; Bercea, C.-I. ; Chan, E. ; Schnabel, J.A.

Interpretable representation learning of cardiac MRI via attribute regularization.

In: (Medical Image Computing and Computer Assisted Intervention – MICCAI 2024). Berlin [u.a.]: Springer, 2024. 492-501 (Lect. Notes Comput. Sc. ; 15010)
DOI
Interpretability is essential in medical imaging to ensure that clinicians can comprehend and trust artificial intelligence models. Several approaches have been recently considered to encode attributes in the latent space to enhance its interpretability. Notably, attribute regularization aims to encode a set of attributes along the dimensions of a latent representation. However, this approach is based on Variational AutoEncoder and suffers from blurry reconstruction. In this paper, we propose an Attributed-regularized Soft Introspective Variational Autoencoder that combines attribute regularization of the latent space within the framework of an adversarially trained variational autoencoder. We demonstrate on short-axis cardiac Magnetic Resonance images of the UK Biobank the ability of the proposed method to address blurry reconstruction issues of variational autoencoder methods while preserving the latent space interpretability.
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Publication type Article: Conference contribution
Corresponding Author
Keywords Cardiac Imaging; Interpretability
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Conference Title Medical Image Computing and Computer Assisted Intervention – MICCAI 2024
Journal Lecture Notes in Computer Science
Quellenangaben Volume: 15010, Issue: , Pages: 492-501 Article Number: , Supplement: ,
Publisher Springer
Publishing Place Berlin [u.a.]
Non-patent literature Publications
Institute(s) Institute for Machine Learning in Biomed Imaging (IML)
Helmholtz Artifical Intelligence Cooperation Unit (HAICU)
Grants Helmholtz Association