Feasibility and effectiveness of automatic deep learning network and radiomics models for differentiating tumor stroma ratio in pancreatic ductal adenocarcinoma.
- DOI
- 10.1186/s13244-023-01553-z
- Published
- 2023 Dec 21
- Container
- Insights into imaging
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1186/s13244-023-01553-z,
title = {Feasibility and effectiveness of automatic deep learning network and radiomics models for differentiating tumor stroma ratio in pancreatic ductal adenocarcinoma.},
author = {Liao H and Yuan J and Liu C and Zhang J and Yang Y and Liang H and Jiang S and Chen S and Li Y and Liu Y},
year = {2023},
journal = {Insights into imaging},
doi = {10.1186/s13244-023-01553-z},
url = {https://doi.org/10.1186/s13244-023-01553-z}
}RIS
TY - JOUR TI - Feasibility and effectiveness of automatic deep learning network and radiomics models for differentiating tumor stroma ratio in pancreatic ductal adenocarcinoma. AU - Liao H AU - Yuan J AU - Liu C AU - Zhang J AU - Yang Y AU - Liang H AU - Jiang S AU - Chen S AU - Li Y AU - Liu Y PY - 2023 JO - Insights into imaging DO - 10.1186/s13244-023-01553-z UR - https://doi.org/10.1186/s13244-023-01553-z ER -
APA
H, L., J, Y., C, L., J, Z., Y, Y., H, L., S, J., S, C., Y, L., & Y, L. (2023). Feasibility and effectiveness of automatic deep learning network and radiomics models for differentiating tumor stroma ratio in pancreatic ductal adenocarcinoma.. Insights into imaging. https://doi.org/10.1186/s13244-023-01553-z
Source records
- pubmed · retrieved 2026-09-26T16:00:07.938Z