Explainability and controllability of patient-specific deep learning with attention-based augmentation for markerless image-guided radiotherapy.

Terunuma T, Sakae T, Hu Y, Takei H, Moriya S, Okumura T, Sakurai H

DOI: 10.1002/mp.16095
Published: 2023 Jan
Container: Medical physics
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.1002/mp.16095,
  title = {Explainability and controllability of patient-specific deep learning with attention-based augmentation for markerless image-guided radiotherapy.},
  author = {Terunuma T and Sakae T and Hu Y and Takei H and Moriya S and Okumura T and Sakurai H},
  year = {2023},
  journal = {Medical physics},
  doi = {10.1002/mp.16095},
  url = {https://doi.org/10.1002/mp.16095}
}

RIS

TY  - JOUR
TI  - Explainability and controllability of patient-specific deep learning with attention-based augmentation for markerless image-guided radiotherapy.
AU  - Terunuma T
AU  - Sakae T
AU  - Hu Y
AU  - Takei H
AU  - Moriya S
AU  - Okumura T
AU  - Sakurai H
PY  - 2023
JO  - Medical physics
DO  - 10.1002/mp.16095
UR  - https://doi.org/10.1002/mp.16095
ER  -

APA

T, T., T, S., Y, H., H, T., S, M., T, O., & H, S. (2023). Explainability and controllability of patient-specific deep learning with attention-based augmentation for markerless image-guided radiotherapy.. Medical physics. https://doi.org/10.1002/mp.16095

Source records

pubmed · retrieved 2026-09-26T03:37:41.790Z
europe-pmc · retrieved 2026-09-26T03:37:41.811Z