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Analyzing Heterogeneity in Neuroimaging with Probabilistic Multivariate Clustering Approaches
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Analyzing Heterogeneity in Neuroimaging with Probabilistic Multivariate Clustering Approaches. |
| 개인저자 | Dong, Aoyan. |
| 단체저자명 | University of Pennsylvania. Electrical and Systems Engineering. |
| 발행사항 | [S.l.]: University of Pennsylvania., 2017. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2017. |
| 형태사항 | 143 p. |
| 기본자료 저록 | Dissertation Abstracts International 79-07B(E). Dissertation Abstract International |
| ISBN | 9780355618440 |
| 학위논문주기 | Thesis (Ph.D.)--University of Pennsylvania, 2017. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 79-07(E), Section: B.
Adviser: Christos Davatzikos. |
| 이용제한사항 | This item is not available from ProQuest Dissertations & Theses. |
| 요약 | Automated quantitative neuroimaging analysis methods have been crucial in elucidating normal and pathological brain structure and function, and in building in vivo markers of disease and its progression. Commonly used methods can identify and pr |
| 요약 | In this thesis, we leveraged machine learning techniques to develop novel tools that can analyze the heterogeneity in both cross-sectional and longitudinal neuroimaging studies. Specifically, we developed a semi-supervised clustering method for |
| 요약 | The proposed tools were extensively validated using synthetic data. Importantly, they were applied to study the heterogeneity in large clinical neuroimaging cohorts. We identified four disease subtypes with distinct imaging signatures using data |
| 일반주제명 | Electrical engineering. Medical imaging. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
