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Activity Number:
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88
- Recent Advances in Multimodal Neuroimaging Data Integration
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Type:
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Invited
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Date/Time:
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Monday, August 8, 2022 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistics in Imaging
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Abstract #320393
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Title:
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Supervised and Unsupervised Learning for Multimodal Data Integration
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Author(s):
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Brian Caffo*
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Companies:
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Johns Hopkins University
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Keywords:
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multimodal;
imaging;
fmri;
mri;
neuroscience;
machine learning
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Abstract:
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In this talk we consider variations on supervised and unsupervised learning techniques for integrating multiple data modalities in a single analysis. We first examine extensions of regression and principal components and then extend the methods to neural networks. We propose embedding structured linear and non-linear transformations of the high dimensional data within traditional approaches as a key component of achieving explainable multi-modal models. We apply the developed methods to experiments in computational biology including neuroimaging and genomic measurements.
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Authors who are presenting talks have a * after their name.
