A coarse‐to‐fine framework combining deep learning and Monte Carlo for BNCT patient position optimization toward inverse planning
- DOI
- 10.1002/mp.70642
- Published
- 2026-08-20
- Container
- Medical Physics
- Publisher
- Wiley
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1002/mp.70642,
title = {A coarse‐to‐fine framework combining deep learning and Monte Carlo for BNCT patient position optimization toward inverse planning},
author = {Yoonho Na and Chang‐min Lee and Kyuri Kim and Hyungjoo Cho and Jimin Lee and Sung‐Joon Ye},
year = {2026},
journal = {Medical Physics},
doi = {10.1002/mp.70642},
url = {https://doi.org/10.1002/mp.70642}
}RIS
TY - JOUR TI - A coarse‐to‐fine framework combining deep learning and Monte Carlo for BNCT patient position optimization toward inverse planning AU - Yoonho Na AU - Chang‐min Lee AU - Kyuri Kim AU - Hyungjoo Cho AU - Jimin Lee AU - Sung‐Joon Ye PY - 2026 JO - Medical Physics DO - 10.1002/mp.70642 UR - https://doi.org/10.1002/mp.70642 ER -
APA
Na, Y., Lee, C., Kim, K., Cho, H., Lee, J., & Ye, S. (2026). A coarse‐to‐fine framework combining deep learning and Monte Carlo for BNCT patient position optimization toward inverse planning. Medical Physics. https://doi.org/10.1002/mp.70642
Source records
- crossref · retrieved 2026-09-25T16:19:06.910Z