Simulated clinical deployment of fully automatic deep learning for clinical prostate MRI assessment

OBJECTIVES: To simulate clinical deployment, evaluate performance, and establish quality assurance of a deep learning algorithm (U-Net) for detection, localization, and segmentation of clinically significant prostate cancer (sPC), ISUP grade group ≥ 2, using bi-parametric MRI. METHODS: In 2017, 284...

Popoln opis

Shranjeno v:
Bibliografske podrobnosti
izdano v:Eur Radiol
Main Authors: Schelb, Patrick, Wang, Xianfeng, Radtke, Jan Philipp, Wiesenfarth, Manuel, Kickingereder, Philipp, Stenzinger, Albrecht, Hohenfellner, Markus, Schlemmer, Heinz-Peter, Maier-Hein, Klaus H., Bonekamp, David
Format: Artigo
Jezik:Inglês
Izdano: Springer Berlin Heidelberg 2020
Teme:
Urogenital
Online dostop:https://ncbi.nlm.nih.gov/pmc/articles/PMC7755653/
https://ncbi.nlm.nih.gov/pubmed/32767102
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1007/s00330-020-07086-z
Oznake: Označite
Brez oznak, prvi označite!

Podobne knjige/članki