Multimodal fusion using sparse CCA for breast cancer survival prediction
- URL: http://arxiv.org/abs/2103.05432v1
- Date: Tue, 9 Mar 2021 14:23:50 GMT
- Title: Multimodal fusion using sparse CCA for breast cancer survival prediction
- Authors: Vaishnavi Subramanian, Tanveer Syeda-Mahmood, Minh N. Do
- Abstract summary: 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.
- Score: 18.586974977393258
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: 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.
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