MM-SurvNet: Deep Learning-Based Survival Risk Stratification in Breast Cancer Through Multimodal Data Fusion
<|reference_start|>MM-SurvNet: Deep Learning-Based Survival Risk Stratification in Breast Cancer Through Multimodal Data Fusion: 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.<|reference_end|>
@article{mondol2024mm-survnet:,
    title={MM-SurvNet: Deep Learning-Based Survival Risk Stratification in Breast
  Cancer Through Multimodal Data Fusion},
    author={Raktim Kumar Mondol, Ewan K.A. Millar, Arcot Sowmya, Erik Meijering},
    journal={arXiv preprint arXiv:2402.11788},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.11788},
    primaryClass={cs.CV cs.AI}
}