The impact of data consistency on deep learning models for nasopharyngeal cancer organ auto-segmentation.
- DOI
- 10.1088/2057-1976/ae596e
- Published
- 2026 Apr 10
- Container
- Biomedical physics & engineering express
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1088/2057-1976/ae596e,
title = {The impact of data consistency on deep learning models for nasopharyngeal cancer organ auto-segmentation.},
author = {Fang Y and Wang J and He X and Hu C and Yu L and Guo Y and Zhong Y and Mi J and Chen S and Qiao J and Yang Y and Hu W},
year = {2026},
journal = {Biomedical physics \& engineering express},
doi = {10.1088/2057-1976/ae596e},
url = {https://doi.org/10.1088/2057-1976/ae596e}
}RIS
TY - JOUR TI - The impact of data consistency on deep learning models for nasopharyngeal cancer organ auto-segmentation. AU - Fang Y AU - Wang J AU - He X AU - Hu C AU - Yu L AU - Guo Y AU - Zhong Y AU - Mi J AU - Chen S AU - Qiao J AU - Yang Y AU - Hu W PY - 2026 JO - Biomedical physics & engineering express DO - 10.1088/2057-1976/ae596e UR - https://doi.org/10.1088/2057-1976/ae596e ER -
APA
Y, F., J, W., X, H., C, H., L, Y., Y, G., Y, Z., J, M., S, C., J, Q., Y, Y., & W, H. (2026). The impact of data consistency on deep learning models for nasopharyngeal cancer organ auto-segmentation.. Biomedical physics & engineering express. https://doi.org/10.1088/2057-1976/ae596e
Source records
- pubmed · retrieved 2026-09-26T21:01:50.288Z