Lorenzi, M;
Deprez, M;
Balelli, I;
Aguila, AL;
Altmann, A;
(2023)
Integration of Multimodal Data.
In: Colliot, O, (ed.)
Neuromethods.
(pp. 573-597).
Humana: New York, NY, USA.
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Abstract
This chapter focuses on the joint modeling of heterogeneous information, such as imaging, clinical, and biological data. This kind of problem requires to generalize classical uni- and multivariate association models to account for complex data structure and interactions, as well as high data dimensionality. Typical approaches are essentially based on the identification of latent modes of maximal statistical association between different sets of features and ultimately allow to identify joint patterns of variations between different data modalities, as well as to predict a target modality conditioned on the available ones. This rationale can be extended to account for several data modalities jointly, to define multi-view, or multi-channel, representation of multiple modalities. This chapter covers both classical approaches such as partial least squares (PLS) and canonical correlation analysis (CCA), along with most recent advances based on multi-channel variational autoencoders. Specific attention is here devoted to the problem of interpretability and generalization of such high-dimensional models. These methods are illustrated in different medical imaging applications, and in the joint analysis of imaging and non-imaging information, such as -omics or clinical data.
| Type: | Book chapter |
|---|---|
| Title: | Integration of Multimodal Data |
| ISBN-13: | 9781071631942 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/978-1-0716-3195-9_19 |
| Publisher version: | http://dx.doi.org/10.1007/978-1-0716-3195-9_19 |
| Language: | English |
| Additional information: | Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. |
| Keywords: | Multivariate analysis, Latent variable models, Multimodal imaging, -Omics, Imaginggenetics, Partial least squares, Canonical correlation analysis, Variational autoencoders, Sparsity, Interpretability |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10201183 |
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