Multimodal fusion using sparse CCA for breast cancer survival prediction

Effective understanding of a disease such as cancer requires fusing multiple sources of information captured across physical scales by multimodal data. In this work, we propose a novel feature embedding module that derives from canonical correlation analyses to account for intra-modality and inter-m...

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Bibliographic Details
Published in:arXiv.org 2021-03
Main Authors: Subramanian, Vaishnavi, Syeda-Mahmood, Tanveer, Do, Minh N
Format: Article
Language:English
Subjects:
Breast cancer
Correlation analysis
Modules
Survival
Online Access:Get full text
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Summary:Effective understanding of a disease such as cancer requires fusing multiple sources of information captured across physical scales by multimodal data. In this work, we propose a novel feature embedding module that derives from canonical correlation analyses to account for intra-modality and inter-modality correlations. Experiments on simulated and real data demonstrate how our proposed module can learn well-correlated multi-dimensional embeddings. These embeddings perform competitively on one-year survival classification of TCGA-BRCA breast cancer patients, yielding average F1 scores up to 58.69% under 5-fold cross-validation.
ISSN:2331-8422