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Voxel-wise Classification of Prostate Cancer Using Multi-parametric MRI Data
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Voxel-wise Classification of Prostate Cancer Using Multi-parametric MRI Data. |
| 개인저자 | Jin, Jin. |
| 단체저자명 | University of Minnesota. Biostatistics. |
| 발행사항 | [S.l.]: University of Minnesota., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 153 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-04B. Dissertation Abstract International |
| ISBN | 9781088320938 |
| 학위논문주기 | Thesis (Ph.D.)--University of Minnesota, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-04, Section: B.
Advisor: Koopmeiners, Joseph |
| 이용제한사항 | This item must not be sold to any third party vendors. |
| 요약 | As 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 |
| 일반주제명 | Biostatistics. |
| 언어 | 영어 |
| 바로가기 | 
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