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008181129s2017 ||| | | | eng d
020 ▼a 9780355618440
035 ▼a (MiAaPQ)AAI10636610
035 ▼a (MiAaPQ)upenngdas:13021
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0491 ▼f DP
0820 ▼a 621.3
1001 ▼a Dong, Aoyan.
24510 ▼a Analyzing Heterogeneity in Neuroimaging with Probabilistic Multivariate Clustering Approaches.
260 ▼a [S.l.] : ▼b University of Pennsylvania., ▼c 2017
260 1 ▼a Ann Arbor : ▼b ProQuest Dissertations & Theses, ▼c 2017
300 ▼a 143 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 79-07(E), Section: B.
500 ▼a Adviser: Christos Davatzikos.
5021 ▼a Thesis (Ph.D.)--University of Pennsylvania, 2017.
506 ▼a This item is not available from ProQuest Dissertations & Theses.
520 ▼a 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
520 ▼a 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
520 ▼a 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
590 ▼a School code: 0175.
650 4 ▼a Electrical engineering.
650 4 ▼a Medical imaging.
690 ▼a 0544
690 ▼a 0574
71020 ▼a University of Pennsylvania. ▼b Electrical and Systems Engineering.
7730 ▼t Dissertation Abstracts International ▼g 79-07B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0175
791 ▼a Ph.D.
792 ▼a 2017
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T14996672 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자 ▼b 관리자