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https://scholarbank.nus.edu.sg/handle/10635/186363
DC Field | Value | |
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dc.title | DEEP LEARNING FOR FUNCTIONAL CONNECTIVITY PREDICTION OF NON-BRAIN-IMAGING PHENOTYPES | |
dc.contributor.author | HE TONG | |
dc.date.accessioned | 2021-02-14T18:00:27Z | |
dc.date.available | 2021-02-14T18:00:27Z | |
dc.date.issued | 2020-08-07 | |
dc.identifier.citation | HE TONG (2020-08-07). DEEP LEARNING FOR FUNCTIONAL CONNECTIVITY PREDICTION OF NON-BRAIN-IMAGING PHENOTYPES. ScholarBank@NUS Repository. | |
dc.identifier.uri | https://scholarbank.nus.edu.sg/handle/10635/186363 | |
dc.description.abstract | Machine learning is an important tool powering many aspects of neuroscience research. A growing literature applies classical machine learning algorithms and deep neural networks (DNNs) to a variety of brain imaging applications. In this thesis, we compared kernel ridge regression with three types of DNN architectures in RSFC-based phenotypic prediction. This is one of the largest empirical evaluations of DNN’s utility in RSFC-based fingerprinting. We showed that kernel ridge regression and three DNNs achieved similar performance for RSFC-based prediction of a wide range of phenotypes. Then, we proposed a novel and simple approach, meta-matching, which leverages the power of large datasets to boost the RSFC-based prediction of unseen non-brain-imaging phenotypes (nBIPs) in small-scale studies. Meta-matching can be utilized in conjunction with any machine learning algorithm. We showed that meta-matching dramatically improved RSFC-based phenotypic prediction on new unseen phenotypes with small sample sizes. | |
dc.language.iso | en | |
dc.subject | Fingerprinting, deep learning, machine learning, meta learning, transfer learning, kernel ridge regression | |
dc.type | Thesis | |
dc.contributor.department | ELECTRICAL & COMPUTER ENGINEERING | |
dc.contributor.supervisor | Boon Thye Thomas Yeo | |
dc.description.degree | Ph.D | |
dc.description.degreeconferred | DOCTOR OF PHILOSOPHY (FOE) | |
Appears in Collections: | Ph.D Theses (Open) |
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