# MM-SurvNet: Deep Learning-Based Survival Risk Stratification in Breast Cancer Through Multimodal Data Fusion

arXiv: https://arxiv.org/abs/2402.11788 · HTML: https://mlsanity.com/paper/2402.11788/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 31 references that are in the map.
- First posted on arXiv 19 Feb 2024.

## Builds on (most cited first, 3 of 3)

- [MaxViT: Multi-Axis Vision Transformer](https://mlsanity.com/paper/2204.01697/) (2022)
- [Multimodal fusion using sparse CCA for breast cancer survival prediction](https://mlsanity.com/paper/2103.05432/) (2021)
- [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)

## Built on by (most cited first, 2 of 2)

- [Advancing Histopathology-Based Breast Cancer Diagnosis: Insights into Multi-Modality and Explainability](https://mlsanity.com/paper/2406.12897/) (2024)
- [A Systematic Review on the Generative AI Applications in Human Medical Genomics](https://mlsanity.com/paper/2508.20275/) (2025)

## Abstract (arXiv)

Survival risk stratification is an important step in clinical decision making for breast cancer management. We propose a novel deep learning approach for this purpose by integrating histopathological imaging, genetic and clinical data. It employs vision transformers, specifically the MaxViT model, for image feature extraction, and self-attention to capture intricate image relationships at the patient level. A dual cross-attention mechanism fuses these features with genetic data, while clinical data is incorporated at the final layer to enhance predictive accuracy. Experiments on the public TCGA-BRCA dataset show that our model, trained using the negative log likelihood loss function, can achieve superior performance with a mean C-index of 0.64, surpassing existing methods. This advancement facilitates tailored treatment strategies, potentially leading to improved patient outcomes.