<div class="csl-bib-body">
<div class="csl-entry">Licandro, R. (2021). <i>Spatio temporal modelling of dynamic developmental patterns</i> [Dissertation, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2021.39603</div>
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dc.identifier.uri
https://doi.org/10.34726/hss.2021.39603
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/17533
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dc.description.abstract
Spatio Temporal Modelling plays an important role in personalized medicine, virtual clinical trials or drug target identification. It enables the encoding of trajectories of complex diseases, metabolic or developmental pathways, to optimise an individual’s disease treatment or determine a developmental status. Dynamic Developmental Patterns (DDP) form the main challenge in modelling trajectories, constituted of the incompleteness and irregularity of observations, inter-patient variability and impairing factors like comorbidity, age or individual treatment response. The focus of this thesis lies in providing new strategies for the spatio- temporal modelling of dynamic developmental patterns, to encode and understand baseline trajectories disentangled from time-dependent or systemic dynamics. Thus, on the one hand the identification of suitable baseline states is essential and on the other hand the development of techniques to analyse the dynamics’ deviations and relations to the baseline. Here, it is demonstrated that the proposed modelling concept is capable to flexibly model DDPs independent of the imaging modalities, of different populations/age ranges and applications to answer research questions in the field of computer vision, cancer research, brain development and functional connectivity network analysis. It leads to the development of novel data representation forms for DDPs, segmentation strategies, classification procedures and time-dependent prediction approaches, outperforming state of the art methods.
en
dc.language
English
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dc.language.iso
en
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dc.rights.uri
http://rightsstatements.org/vocab/InC/1.0/
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dc.subject
Dynamic developmental pattern analysis
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dc.subject
spatio temporal modelling
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dc.subject
fetal brain imaging
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dc.subject
statistical pattern analysis
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dc.subject
disease progression model
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dc.subject
generative modelling
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dc.subject
atlas learning
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dc.subject
evolution risk prediction
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dc.subject
plasticity
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dc.subject
leukaemia
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dc.title
Spatio temporal modelling of dynamic developmental patterns
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dc.type
Thesis
en
dc.type
Hochschulschrift
de
dc.rights.license
In Copyright
en
dc.rights.license
Urheberrechtsschutz
de
dc.identifier.doi
10.34726/hss.2021.39603
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dc.contributor.affiliation
TU Wien, Österreich
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dc.rights.holder
Roxane Licandro
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dc.publisher.place
Wien
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tuw.version
vor
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tuw.thesisinformation
Technische Universität Wien
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dc.contributor.assistant
Langs, Georg
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tuw.publication.orgunit
E193 - Institut für Visual Computing and Human-Centered Technology
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dc.type.qualificationlevel
Doctoral
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dc.identifier.libraryid
AC16210309
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dc.description.numberOfPages
168
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dc.thesistype
Dissertation
de
dc.thesistype
Dissertation
en
tuw.author.orcid
0000-0001-9066-4473
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dc.rights.identifier
In Copyright
en
dc.rights.identifier
Urheberrechtsschutz
de
tuw.advisor.staffStatus
staff
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tuw.assistant.staffStatus
staff
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tuw.advisor.orcid
0000-0002-5217-2854
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item.openaccessfulltext
Open Access
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item.openairecristype
http://purl.org/coar/resource_type/c_db06
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item.grantfulltext
open
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item.mimetype
application/pdf
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item.languageiso639-1
en
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item.openairetype
doctoral thesis
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item.fulltext
with Fulltext
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item.cerifentitytype
Publications
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crisitem.author.dept
E193-01 - Forschungsbereich Computer Vision
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crisitem.author.orcid
0000-0001-9066-4473
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crisitem.author.parentorg
E193 - Institut für Visual Computing and Human-Centered Technology