End‐to‐end deep learning approach for uterine artery‐ovarian artery anastomosis detection from digital subtraction angiography sequences
- DOI
- 10.1002/mp.70083
- Published
- 2025-10-24
- Container
- Medical Physics
- Publisher
- Wiley
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1002/mp.70083,
title = {End‐to‐end deep learning approach for uterine artery‐ovarian artery anastomosis detection from digital subtraction angiography sequences},
author = {Yu Lei and Tao Sun and Yanqiao Ren and Dongqiao Xiang and Lei Chen and Yi Li and Xiaoyun Liang and Huangxuan Zhao and Jinqiang Ma and Chuansheng Zheng},
year = {2025},
journal = {Medical Physics},
doi = {10.1002/mp.70083},
url = {https://doi.org/10.1002/mp.70083}
}RIS
TY - JOUR TI - End‐to‐end deep learning approach for uterine artery‐ovarian artery anastomosis detection from digital subtraction angiography sequences AU - Yu Lei AU - Tao Sun AU - Yanqiao Ren AU - Dongqiao Xiang AU - Lei Chen AU - Yi Li AU - Xiaoyun Liang AU - Huangxuan Zhao AU - Jinqiang Ma AU - Chuansheng Zheng PY - 2025 JO - Medical Physics DO - 10.1002/mp.70083 UR - https://doi.org/10.1002/mp.70083 ER -
APA
Lei, Y., Sun, T., Ren, Y., Xiang, D., Chen, L., Li, Y., Liang, X., Zhao, H., Ma, J., & Zheng, C. (2025). End‐to‐end deep learning approach for uterine artery‐ovarian artery anastomosis detection from digital subtraction angiography sequences. Medical Physics. https://doi.org/10.1002/mp.70083
Source records
- crossref · retrieved 2026-09-25T14:46:05.782Z