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Voxel-wise Classification of Prostate Cancer Using Multi-parametric MRI Data

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서명/저자사항Voxel-wise Classification of Prostate Cancer Using Multi-parametric MRI Data.
개인저자Jin, Jin.
단체저자명University of Minnesota. Biostatistics.
발행사항[S.l.]: University of Minnesota., 2019.
발행사항Ann Arbor: ProQuest Dissertations & Theses, 2019.
형태사항153 p.
기본자료 저록Dissertations Abstracts International 81-04B.
Dissertation Abstract International
ISBN9781088320938
학위논문주기Thesis (Ph.D.)--University of Minnesota, 2019.
일반주기 Source: Dissertations Abstracts International, Volume: 81-04, Section: B.
Advisor: Koopmeiners, Joseph
이용제한사항This item must not be sold to any third party vendors.
요약As a continuously developing tool for the diagnosis and prognosis of prostate cancer, multi-parametric magnetic resonance imaging (mpMRI) has been widely used in a variety of prostate cancer-related topics. While current research has shown the great potential of mpMRI in detecting prostate cancer, further investigation is needed for modeling some specific features of mpMRI, including the anatomic difference between different regions of a prostate, the spatial correlation between voxels within each prostate image, and the difference in the distribution of the observed mpMRI parameters between patients.This dissertation focuses on novel statistical methods for the voxel-wise classification of prostate cancer using mpMRI data. Systematic modeling frameworks will be proposed to improve cancer classification by incorporating the aforementioned features of mpMRI. Three topics are discussed in depth: (1) development of a general Bayesian modeling framework that can incorporate the various mpMRI features
일반주제명Biostatistics.
언어영어
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