Dataset Open Access
Kieslich, Aaron Markus;
Singh, Yerik;
Palkowitsch, Martina;
Starke, Sebastian;
Hennings, Fabian;
Troost, Esther Gera Cornelia;
Krause, Mechthild;
Bensberg, Jona;
Lühr, Armin;
Heinzelmann, Feline;
Bäumer, Christian;
Timmermann, Beate;
Depauw, Nicolas;
Shih, Helen A.;
Löck, Steffen
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<dct:title>Data publication: Deep learning for dose-averaged linear energy transfer estimation in pencil-beam scanning and double scattering proton plans with uncertainty-aware external validation</dct:title>
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<dcat:keyword>proton radiotherapy</dcat:keyword>
<dcat:keyword>linear energy transfer</dcat:keyword>
<dcat:keyword>deep learning</dcat:keyword>
<dcat:keyword>pencil beam scanning</dcat:keyword>
<dcat:keyword>double scattering</dcat:keyword>
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<dct:description><p>This repository contains the outputs, model checkpoints&nbsp;and result data of our deep-learning-based experiments for the approximation of Monte-Carlo-simulated linear energy transfer distributions and uncertainty estimation, which build the foundation for the corresponding article.</p></dct:description>
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