Multimodal fusion using sparse CCA for breast cancer survival prediction
This work addresses survival prediction for breast cancer patients, but it is incremental as it builds on existing multimodal fusion methods with a specific adaptation.
The paper tackled breast cancer survival prediction by fusing multimodal data with a novel feature embedding module based on canonical correlation analysis, achieving an average F1 score of 58.69% for one-year survival classification on TCGA-BRCA data.
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.