Diagnosing uterine cervical cancer on a single T2-weighted image: Comparison between deep learning versus radiologists

A Urushibara, T Saida, K Mori, T Ishiguro… - European Journal of …, 2021 - Elsevier
Purpose To compare deep learning with radiologists when diagnosing uterine cervical
cancer on a single T2-weighted image. Methods This study included 418 patients (age …
被引用次数：29 相关文章

[引用][C] Diagnosing uterine cervical cancer on a single T2-weighted image: Comparison between deep learning versus radiologists

A Urushibara, T Saida, K Mori, T Ishiguro… - European Journal of …, 2021 - cir.nii.ac.jp
Diagnosing uterine cervical cancer on a single T2-weighted image: Comparison between
deep learning versus radiologists | CiNii Research CiNii 国立情報学研究所 学術情報 …

Diagnosing uterine cervical cancer on a single T2-weighted image: Comparison between deep learning versus radiologists.

A Urushibara, T Saida, K Mori, T Ishiguro… - European Journal of …, 2020 - europepmc.org
Purpose To compare deep learning with radiologists when diagnosing uterine cervical
cancer on a single T2-weighted image. Methods This study included 418 patients (age …

Diagnosing uterine cervical cancer on a single T2-weighted image: Comparison between deep learning versus radiologists

A Urushibara, T Saida, K Mori… - … journal of radiology, 2021 - pubmed.ncbi.nlm.nih.gov
Purpose To compare deep learning with radiologists when diagnosing uterine cervical
cancer on a single T2-weighted image. Methods This study included 418 patients (age …

Diagnosing uterine cervical cancer on a single T2-weighted image: Comparison between deep learning versus radiologists

A Urushibara, T Saida, K Mori, T Ishiguro… - European Journal of …, 2021 - inis.iaea.org
[en] Highlights:• A deep learning model using convolutional neural networks (DCNN) can
diagnose uterine cervical cancer on a T2-weighted image.• The DCNN model, built from less …

Diagnosing uterine cervical cancer on a single T2-weighted image: Comparison between deep learning versus radiologists

A Urushibara, T Saida, K Mori, T Ishiguro… - European Journal of …, 2021 - ejradiology.com
Purpose To compare deep learning with radiologists when diagnosing uterine cervical
cancer on a single T2-weighted image. Methods This study included 418 patients (age …