Multimodal fusion using sparse CCA for breast cancer survival prediction

TL;DR

This paper introduces a novel multimodal data fusion method using sparse canonical correlation analysis to improve breast cancer survival prediction, demonstrating competitive results on real patient data.

Contribution

The paper presents a new feature embedding module based on canonical correlation analysis for multimodal data fusion in cancer prognosis.

Findings

Embeddings learned are well-correlated across modalities.

Achieved up to 58.69% F1 score in survival prediction.

Method performs well on simulated and real data.

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.