Supplemental Data to Submission Mattes et al. IEEE TMI 2020

The data uploaded here shall support the paperDecision Tree Analysis of Geometric and Hemodynamic Parameters as Indicators for Complications after Endovascular Aortic Aneurysm Repairwhich has been submitted to IEEE Transactions on Medical Imaging (2020, September 25) by the authorsJulian Mattes, Wol...

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Bibliographische Detailangaben
Hauptverfasser:	Mattes, Julian, Fenz, Wolfgang
Format:	Dataset
Sprache:	eng
Schlagworte:	aortic aneurysm Artificial Intelligence biomechanical parameters Computational Intelligence Computer Vision Decision Tree endovascular aortic repair feature selection Health Machine Learning Medical Imaging shape descriptors stent-graft supervised statistical learning
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Beschreibung
Zusammenfassung:	The data uploaded here shall support the paperDecision Tree Analysis of Geometric and Hemodynamic Parameters as Indicators for Complications after Endovascular Aortic Aneurysm Repairwhich has been submitted to IEEE Transactions on Medical Imaging (2020, September 25) by the authorsJulian Mattes, Wolfgang Fenz, Stefan Thumfart, Gerhard Haitchi, Pierre Schmit, Franz A. FellnerDuring review the data shall only be visible for the reviewers of this paper. Afterwards this abstract will be modified and complemented and a dataset image will be uploaded.
DOI:	10.21227/9db9-ne03