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Test-retest repeatability of a deep learning architecture in detecting and segmenting clinically significant prostate cancer on apparent diffusion coefficient (ADC) maps

OBJECTIVES: To evaluate short-term test-retest repeatability of a deep learning architecture (U-Net) in slice- and lesion-level detection and segmentation of clinically significant prostate cancer (csPCa: Gleason grade group > 1) using diffusion-weighted imaging fitted with monoexponential functi...

詳細記述

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書誌詳細
出版年:Eur Radiol
主要な著者: Hiremath, Amogh, Shiradkar, Rakesh, Merisaari, Harri, Prasanna, Prateek, Ettala, Otto, Taimen, Pekka, Aronen, Hannu J., Boström, Peter J., Jambor, Ivan, Madabhushi, Anant
フォーマット: Artigo
言語:Inglês
出版事項: 2020
主題:
オンライン・アクセス:https://ncbi.nlm.nih.gov/pmc/articles/PMC7821380/
https://ncbi.nlm.nih.gov/pubmed/32700021
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1007/s00330-020-07065-4
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