Donini, M;
Monteiro, JM;
Pontil, M;
Hahn, T;
Fallgatter, AJ;
Shawe-Taylor, J;
Mourão-Miranda, J;
(2019)
Combining heterogeneous data sources for neuroimaging based diagnosis: re-weighting and selecting what is important.
Neuroimage
, 195
pp. 215-231.
10.1016/j.neuroimage.2019.01.053.
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Abstract
Combining neuroimaging and clinical information for diagnosis, as for example behavioral tasks and genetics characteristics, is potentially beneficial but presents challenges in terms of finding the best data representation for the different sources of information. Their simple combination usually does not provide an improvement if compared with using the best source alone. In this paper, we proposed a framework based on a recent multiple kernel learning algorithm called EasyMKL and we investigated the benefits of this approach for diagnosing two different mental health diseases. The well known Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset tackling the Alzheimer Disease (AD) patients versus healthy controls classification task, and a second dataset tackling the task of classifying an heterogeneous group of depressed patients versus healthy controls. We used EasyMKL to combine a huge amount of basic kernels alongside a feature selection methodology, pursuing an optimal and sparse solution to facilitate interpretability. Our results show that the proposed approach, called EasyMKLFS, outperforms baselines (e.g. SVM and SimpleMKL), state-of-the-art random forests (RF) and feature selection (FS) methods.
| Type: | Article |
|---|---|
| Title: | Combining heterogeneous data sources for neuroimaging based diagnosis: re-weighting and selecting what is important |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.neuroimage.2019.01.053 |
| Publisher version: | https://doi.org/10.1016/j.neuroimage.2019.01.053 |
| Language: | English |
| Additional information: | © 2019 The Author(s). This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
| Keywords: | Feature selection, Multiple kernel learning, Neuroimaging |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10073011 |
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