Radiomic and Genomic Machine Learning Method Performance for Prostate Cancer Diagnosis: Systematic Literature Review

• 主要な著者: Castaldo, Rossana (著者), Cavaliere, Carlo (著者), Soricelli, Andrea (著者), Salvatore, Marco (著者), Pecchia, Leandro (著者), Franzese, Monica (著者)
• フォーマット: 図書
• 出版事項: JMIR Publications, 2021-04-01T00:00:00Z.
• 主題: article
• オンライン･アクセス: Connect to this object online.

BackgroundMachine learning algorithms have been drawing attention at the joining of pathology and radiology in prostate cancer research. However, due to their algorithmic learning complexity and the variability of their architecture, there is an ongoing need to analyze their performance. ObjectiveTh...