# Multimodal fusion using sparse CCA for breast cancer survival prediction

arXiv: https://arxiv.org/abs/2103.05432 · HTML: https://mlsanity.com/paper/2103.05432/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 12 references that are in the map.
- First posted on arXiv 9 Mar 2021.

## Builds on (most cited first, 2 of 2)

- [Multimodal fusion of imaging and genomics for lung cancer recurrence prediction](https://mlsanity.com/paper/2002.01982/) (2020)
- [Correlating Cellular Features with Gene Expression using CCA](https://mlsanity.com/paper/1802.08910/) (2018)

## Built on by (most cited first, 6 of 6)

- [SurvMamba: State Space Model with Multi-grained Multi-modal Interaction for Survival Prediction](https://mlsanity.com/paper/2404.08027/) (2024)
- [Deep Biological Pathway Informed Pathology-Genomic Multimodal Survival Prediction](https://mlsanity.com/paper/2301.02383/) (2023)
- [Multi-modality fusion using canonical correlation analysis methods: Application in breast cancer survival prediction from histology and genomics](https://mlsanity.com/paper/2111.13987/) (2021)
- [Joint Self-Supervised and Supervised Contrastive Learning for Multimodal MRI Data: Towards Predicting Abnormal Neurodevelopment](https://mlsanity.com/paper/2312.15064/) (2023)
- [BioFusionNet: Deep Learning-Based Survival Risk Stratification in ER+ Breast Cancer Through Multifeature and Multimodal Data Fusion](https://mlsanity.com/paper/2402.10717/) (2024)
- [MM-SurvNet: Deep Learning-Based Survival Risk Stratification in Breast Cancer Through Multimodal Data Fusion](https://mlsanity.com/paper/2402.11788/) (2024)

## Abstract (arXiv)

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.