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

Starke, S.; Leger, S.; Zwanenburg, A.; Leger, K.; Lohaus, F.; Linge, A.; Schreiber, A.; Kalinauskaite, G.; Tinhofer, I.; Guberina, N.; Guberina, M.; Balermpas, P.; von der Grün, J.; Ganswindt, U.; Belka, C.; Peeken, J. C.; Combs, S. E.; Böke, S.; Zips, D.; Richter, C.ORC; Troost, E. G. C.ORC; Krause, M.ORC; Baumann, M.; Löck, S.

These are the results from the analyses presented in a paper submitted to Scientific Reports.

The zip file contains the trained model files and the plots that were used in the manuscript.

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.

Keywords: convolutional neural networks; Keras; Deep learning; head and neck cancer; loco-regional-recurrence; Cox proportional hazards
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2D and 3D convolutional neural networks for outcome … (Id 30750) has used this publication of HZDR-primary research data
  • Reseach data in the HZDR data repository RODARE
    Publication date: 2020-02-27
    DOI: 10.14278/rodare.254
    License: CC-BY-NC-4.0

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Publ.-Id: 30759