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1 PublicationData publication: Multitask learning with convolutional neural networks and vision transformers can improve outcome prediction for head and neck cancer patients
Starke, 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.; Troost, E. G. C.; Krause, M.; Baumann, M.; Löck, S.
This dataset contains the model checkpoints, predictions and performance metrics for the multitask neural networks presented in the corresponding manuscript.
Keywords: survival analysis; vision transformer; convolutional neural network; multitask learning; tumor segmentation; head and neck cancer; Cox proportional hazards; loco-regional control; progression-free survival; discrete-time survival models
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- DOI: 10.3390/cancers15194897 references this (Id 37408) publication
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Multitask Learning with Convolutional Neural Networks and Vision Transformers …
ROBIS: 37388 has used this (Id 37408) publication of HZDR-primary research data
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Reseach data in the HZDR data repository RODARE
Publication date: 2023-08-16 Closed access
DOI: 10.14278/rodare.2437
Versions: 10.14278/rodare.2438
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Permalink: https://www.hzdr.de/publications/Publ-37408
