MM-SurvNet: Deep Learning-Based Survival Risk Stratification in Breast Cancer Through Multimodal Data Fusion
This work addresses personalized treatment planning for breast cancer patients, but it is incremental as it builds on existing multimodal fusion techniques.
The paper tackled survival risk stratification in breast cancer by integrating histopathological imaging, genetic, and clinical data using a deep learning approach, achieving a mean C-index of 0.64 on the TCGA-BRCA dataset, which surpasses existing methods.
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.