| LDR | | 02267nam u200433 4500 |
| 001 | | 000000418185 |
| 005 | | 20190215162643 |
| 008 | | 181129s2017 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780355618440 |
| 035 | |
▼a (MiAaPQ)AAI10636610 |
| 035 | |
▼a (MiAaPQ)upenngdas:13021 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 621.3 |
| 100 | 1 |
▼a Dong, Aoyan. |
| 245 | 10 |
▼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. |
| 502 | 1 |
▼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 |
| 710 | 20 |
▼a University of Pennsylvania.
▼b Electrical and Systems Engineering. |
| 773 | 0 |
▼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 |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14996672
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a ***1012033 |