Dataset Open Access
Starke, Sebastian;
Kieslich, Aaron Markus;
Palkowitsch, Martina;
Hennings, Fabian;
Troost, Esther Gera Cornelia;
Krause, Mechthild;
Bensberg, Jona;
Hahn, Christian;
Heinzelmann, Feline;
Bäumer, Christian;
Lühr, Armin;
Timmermann, Beate;
Löck, Steffen
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"description": "<p>This repository contains the outputs and result data of our deep-learning-based experiments for the approximation of Monte-Carlo-simulated linear energy transfer distributions, which build the foundation for the corresponding article.</p>\n\n<p>The Pytorch checkpoint of our finally chosen SegResNet architecture trained on the UPTD dose distributions is located at dd_pbs/Dose-LETd/clip_let_below_0.04/segresnet/all_trainvalid_data/training/lightning_logs/version_6358843/checkpoints/last.ckpt.</p>\n\n<p> </p>\n\n<p>Moreover, we provide an exemplary data sample from a water phantom for trying our analysis pipeline.</p>",
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"title": "Data publication: A deep-learning-based surrogate model for Monte-Carlo simulations of the linear energy transfer in primary brain tumor patients treated with proton-beam radiotherapy",
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| All versions | This version | |
|---|---|---|
| Views | 1,597 | 852 |
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| Data volume | 11.2 GB | 8.1 GB |
| Unique views | 1,275 | 703 |
| Unique downloads | 57 | 32 |