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

DOI

This repository contains the outputs, model checkpoints 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.

Identifier
DOI https://doi.org/10.14278/rodare.4185
Related Identifier IsIdenticalTo https://www.hzdr.de/publications/Publ-42451
Related Identifier IsReferencedBy https://www.hzdr.de/publications/Publ-42419
Related Identifier IsPartOf https://doi.org/10.14278/rodare.4184
Related Identifier IsPartOf https://rodare.hzdr.de/communities/health
Related Identifier IsPartOf https://rodare.hzdr.de/communities/oncoray
Related Identifier IsPartOf https://rodare.hzdr.de/communities/rodare
Metadata Access https://rodare.hzdr.de/oai2d?verb=GetRecord&metadataPrefix=oai_datacite&identifier=oai:rodare.hzdr.de:4185
Provenance
Creator Kieslich, Aaron Markus ORCID logo; Singh, Yerik; Palkowitsch, Martina (ORCID: 0009-0009-7420-208X); Starke, Sebastian ORCID logo; Hennings, Fabian ORCID logo; Troost, Esther Gera Cornelia ORCID logo; Krause, Mechthild ORCID logo; Bensberg, Jona ORCID logo; Lühr, Armin ORCID logo; Heinzelmann, Feline; Bäumer, Christian ORCID logo; Timmermann, Beate ORCID logo; Depauw, Nicolas ORCID logo; Shih, Helen A. ORCID logo; Löck, Steffen ORCID logo
Publisher Rodare
Publication Year 2025
Rights Creative Commons Attribution 4.0 International; Open Access; https://creativecommons.org/licenses/by/4.0/legalcode; info:eu-repo/semantics/openAccess
OpenAccess true
Contact https://rodare.hzdr.de/support
Representation
Resource Type Dataset
Version Version 1.0.0
Discipline Life Sciences; Natural Sciences; Engineering Sciences