Domain independent post-processing with graph U-nets: applications to electrical impedance tomographic imaging⋆ ⋆SH and BH were supported by the National Institute Of Biomedical Imaging And Bioengineering of the National Institutes of Health under Award Number R21EB028064. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. AH was supported by Academy of Finland projects 338408, 346574, 353093. SH and

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Published in:Physiological measurement 2023-12, Vol.44 (12)
Main Authors: Herzberg, William, Hauptmann, Andreas, Hamilton, Sarah J
Format: Article
Language:English
Subjects:
conductivity
deep learning
electrical impedance tomography
finite element method
graph convolutional networks
post-processing
u-net
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container_title Physiological measurement
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Hauptmann, Andreas
Hamilton, Sarah J
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doi_str_mv 10.1088/1361-6579/ad0b3d
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source Institute of Physics:Jisc Collections:IOP Publishing Read and Publish 2024-2025 (Reading List)
subjects conductivity
deep learning
electrical impedance tomography
finite element method
graph convolutional networks
post-processing
u-net
title Domain independent post-processing with graph U-nets: applications to electrical impedance tomographic imaging⋆ ⋆SH and BH were supported by the National Institute Of Biomedical Imaging And Bioengineering of the National Institutes of Health under Award Number R21EB028064. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. AH was supported by Academy of Finland projects 338408, 346574, 353093. SH and
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