Dataset Open Access
Starke, Sebastian;
Leger, Stefan;
Zwanenburg, Alex;
Leger, Karoline;
Lohaus, Fabian;
Linge, Annett;
Schreiber, Andreas;
Kalinauskaite, Goda;
Tinhofer, Inge;
Guberina, Nika;
Guberina, Maja;
Balermpas, Panagiotis;
Grün, Jens von der;
Ganswindt, Ute;
Belka, Claus;
Peeken, Jan C.;
Combs, Stephanie E.;
Böke, Simon;
Zips, Daniel;
Richter, Christian;
Troost, Esther Gera Cornelia;
Krause, Mechthild;
Baumann, Michael;
Löck, Steffen
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<dct:title>2D and 3D convolutional neural networks for outcome modelling of locally advanced head and neck squamous cell carcinoma</dct:title>
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<dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#gYear">2020</dct:issued>
<dcat:keyword>convolutional neural networks</dcat:keyword>
<dcat:keyword>Keras</dcat:keyword>
<dcat:keyword>Deep learning</dcat:keyword>
<dcat:keyword>head and neck cancer</dcat:keyword>
<dcat:keyword>loco-regional-recurrence</dcat:keyword>
<dcat:keyword>Cox proportional hazards</dcat:keyword>
<dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#date">2020-02-27</dct:issued>
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<dct:description><p>These are the results from the analyses presented in a paper submitted to Scientific Reports.</p> <p>The zip file contains the trained model files and the plots that were used in the manuscript.</p> <p>Code for reproduction of our analyses can be obtained from https://github.com/oncoray/cnn-hnscc. There, you also find instructions on how to load our models.</p></dct:description>
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