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postgraduate thesis: Automated lesion detection and risk assessment of suspected prostate cancer on biparametric MRI using deep learning and radiomics

TitleAutomated lesion detection and risk assessment of suspected prostate cancer on biparametric MRI using deep learning and radiomics
Authors
Hui, Fuk Yuen許福元
Issue Date2023
PublisherThe University of Hong Kong (Pokfulam, Hong Kong)
Citation
Hui, F. Y. [許福元]. (2023). Automated lesion detection and risk assessment of suspected prostate cancer on biparametric MRI using deep learning and radiomics. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR.
DegreeMaster of Medical Sciences
SubjectProstate - Cancer - Magnetic resonance imaging
Dept/ProgramDiagnostic Radiology
Persistent Identifierhttp://hdl.handle.net/10722/335507

 

DC FieldValueLanguage
dc.contributor.authorHui, Fuk Yuen-
dc.contributor.author許福元-
dc.date.accessioned2023-11-21T09:14:06Z-
dc.date.available2023-11-21T09:14:06Z-
dc.date.issued2023-
dc.identifier.citationHui, F. Y. [許福元]. (2023). Automated lesion detection and risk assessment of suspected prostate cancer on biparametric MRI using deep learning and radiomics. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR.-
dc.identifier.urihttp://hdl.handle.net/10722/335507-
dc.languageeng-
dc.publisherThe University of Hong Kong (Pokfulam, Hong Kong)-
dc.relation.ispartofHKU Theses Online (HKUTO)-
dc.rightsThe author retains all proprietary rights, (such as patent rights) and the right to use in future works.-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subject.lcshProstate - Cancer - Magnetic resonance imaging-
dc.titleAutomated lesion detection and risk assessment of suspected prostate cancer on biparametric MRI using deep learning and radiomics-
dc.typePG_Thesis-
dc.description.thesisnameMaster of Medical Sciences-
dc.description.thesislevelMaster-
dc.description.thesisdisciplineDiagnostic Radiology-
dc.description.naturepublished_or_final_version-
dc.date.hkucongregation2023-
dc.identifier.mmsid991044730785403414-

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