<div class="csl-bib-body">
<div class="csl-entry">Kotera, J., Wödlinger, M. G., & Keglevic, M. (2023). Learned Lossy Image Compression for Volumetric Medical Data. In R. Sablatnig & F. Kleber (Eds.), <i>Proceedings of the 26th Computer Vision Winter Workshop (CVWW 2023)</i>. CEUR-WS.org. https://doi.org/10.34726/5302</div>
</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/190689
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dc.identifier.uri
https://doi.org/10.34726/5302
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dc.description.abstract
This work addresses the problem of lossy compression of volumetric images consisting of individual slices such as those produced by CT scans and MRI machines in medical imaging. We propose an extension of a single-image lossy compression method with an autoregressive context module to a sequential encoding of the volumetric slices. In particular, we remove the intra-slice autoregressive relation and instead condition the entropy model of the latent on the previous slice in the sequence. This modification alleviates the typical disadvantages of autoregressive contexts and leads to a significant increase in performance compared to encoding each slice independently. We test the proposed method on a dataset of diverse CT scan images in a setting with an emphasis on high-fidelity reconstruction required in medical imaging and show that it compares favorably against several established state-of-the-art codecs in both performance and runtime.
en
dc.description.sponsorship
European Commission
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dc.language.iso
en
-
dc.relation.ispartofseries
CEUR Workshop Proceedings
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dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
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dc.subject
Learned Image Compression
en
dc.subject
Medical Image Data
en
dc.subject
Deep Learning
en
dc.title
Learned Lossy Image Compression for Volumetric Medical Data
en
dc.type
Inproceedings
en
dc.type
Konferenzbeitrag
de
dc.rights.license
Creative Commons Namensnennung 4.0 International
de
dc.rights.license
Creative Commons Attribution 4.0 International
en
dc.identifier.doi
10.34726/5302
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dc.relation.issn
1613-0073
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dc.relation.grantno
GA 965502
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dc.rights.holder
The authors
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dc.type.category
Full-Paper Contribution
-
tuw.booktitle
Proceedings of the 26th Computer Vision Winter Workshop (CVWW 2023)
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tuw.container.volume
3349
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tuw.peerreviewed
true
-
tuw.book.ispartofseries
CEUR Workshop Proceedings
-
tuw.relation.publisher
CEUR-WS.org
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tuw.book.chapter
9
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tuw.project.title
KI-basierte Videokomprimierung für neue Technologien
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tuw.researchTopic.id
I5
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tuw.researchTopic.name
Visual Computing and Human-Centered Technology
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tuw.researchTopic.value
100
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tuw.publication.orgunit
E193-01 - Forschungsbereich Computer Vision
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dc.identifier.libraryid
AC17205088
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dc.description.numberOfPages
9
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tuw.author.orcid
0000-0002-4644-2723
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dc.rights.identifier
CC BY 4.0
de
dc.rights.identifier
CC BY 4.0
en
tuw.editor.orcid
0000-0003-4195-1593
-
tuw.editor.orcid
0000-0001-8351-5066
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tuw.event.name
CVWW 2023: 26th Computer Vision Winter Workshop
en
tuw.event.startdate
15-02-2023
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tuw.event.enddate
17-02-2023
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tuw.event.online
On Site
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tuw.event.type
Event for scientific audience
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tuw.event.place
Krems
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tuw.event.country
AT
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tuw.event.presenter
Kotera, Jan
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wb.sciencebranch
Informatik
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wb.sciencebranch
Mathematik
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wb.sciencebranch.oefos
1020
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wb.sciencebranch.oefos
1010
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wb.sciencebranch.value
90
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wb.sciencebranch.value
10
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item.languageiso639-1
en
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item.openairetype
conference paper
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item.grantfulltext
open
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item.fulltext
with Fulltext
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item.cerifentitytype
Publications
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item.mimetype
application/pdf
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item.openairecristype
http://purl.org/coar/resource_type/c_5794
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item.openaccessfulltext
Open Access
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crisitem.author.dept
E193-01 - Forschungsbereich Computer Vision
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crisitem.author.dept
E193-01 - Forschungsbereich Computer Vision
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crisitem.author.dept
E193-01 - Forschungsbereich Computer Vision
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crisitem.author.orcid
0000-0002-4644-2723
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crisitem.author.parentorg
E193 - Institut für Visual Computing and Human-Centered Technology
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crisitem.author.parentorg
E193 - Institut für Visual Computing and Human-Centered Technology
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crisitem.author.parentorg
E193 - Institut für Visual Computing and Human-Centered Technology