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arXiv · 2103.05432

Multimodal fusion using sparse CCA for breast cancer survival prediction

Vaishnavi Subramanian·Tanveer Syeda-Mahmood·Minh N. Do
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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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Vaishnavi Subramanian, Tanveer Syeda-Mahmood, Minh N. Do. 2021-03-09. Multimodal fusion using sparse CCA for breast cancer survival prediction. https://arxiv.org/abs/2103.05432

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