RadField3D: A Data Generator and Data Format for Deep Learning in Radiation-Protection Dosimetry for Medical Applications
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866917336665030656 |
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| author | Lehner, Felix Lombardo, Pasquale Castillo, Susana Hupe, Oliver Magnor, Marcus |
| author_facet | Lehner, Felix Lombardo, Pasquale Castillo, Susana Hupe, Oliver Magnor, Marcus |
| contents | In this research work, we present our open-source Geant4-based Monte-Carlo simulation application, called RadField3D, for generating threedimensional radiation field datasets for dosimetry. Accompanying, we introduce a fast, machine-interpretable data format with a Python API for easy integration into neural network research, that we call RadFiled3D. Both developments are intended to be used to research alternative radiation simulation methods using deep learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_13852 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | RadField3D: A Data Generator and Data Format for Deep Learning in Radiation-Protection Dosimetry for Medical Applications Lehner, Felix Lombardo, Pasquale Castillo, Susana Hupe, Oliver Magnor, Marcus Machine Learning Computational Physics In this research work, we present our open-source Geant4-based Monte-Carlo simulation application, called RadField3D, for generating threedimensional radiation field datasets for dosimetry. Accompanying, we introduce a fast, machine-interpretable data format with a Python API for easy integration into neural network research, that we call RadFiled3D. Both developments are intended to be used to research alternative radiation simulation methods using deep learning. |
| title | RadField3D: A Data Generator and Data Format for Deep Learning in Radiation-Protection Dosimetry for Medical Applications |
| topic | Machine Learning Computational Physics |
| url | https://arxiv.org/abs/2412.13852 |