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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