Dataset Open Access

2D and 3D convolutional neural networks for outcome modelling of locally advanced head and neck squamous cell carcinoma

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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    <dcat:keyword>convolutional neural networks</dcat:keyword>
    <dcat:keyword>Keras</dcat:keyword>
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    <dcat:keyword>head and neck cancer</dcat:keyword>
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    <dct:description>&lt;p&gt;These are the results from the analyses presented in a paper submitted to Scientific Reports.&lt;/p&gt; &lt;p&gt;The zip file contains the trained model files and the plots that were used in the manuscript.&lt;/p&gt; &lt;p&gt;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.&lt;/p&gt;</dct:description>
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Publication date:
February 27, 2020
DOI:
10.14278/rodare.255

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10.14278/rodare.255

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Keyword(s):
convolutional neural networks Keras Deep learning head and neck cancer loco-regional-recurrence Cox proportional hazards
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https://www.hzdr.de/publications/Publ-30759
Referenced by:
https://www.hzdr.de/publications/Publ-30750
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