Multimodal Deep Learning for Subtype Classification in Breast Cancer Using Histopathological Images and Gene Expression Data
This work addresses the problem of limited predictive power in breast cancer subtyping for personalized treatment, though it is incremental as it combines existing methods like ResNet-50 with a cross-attention mechanism.
The study tackled breast cancer subtype classification by integrating histopathological images and gene expression data using a deep multimodal learning framework, resulting in improved performance over unimodal approaches in accuracy, precision-recall AUC, and F1-score as demonstrated through five-fold cross-validation.
Molecular subtyping of breast cancer is crucial for personalized treatment and prognosis. Traditional classification approaches rely on either histopathological images or gene expression profiling, limiting their predictive power. In this study, we propose a deep multimodal learning framework that integrates histopathological images and gene expression data to classify breast cancer into BRCA.Luminal and BRCA.Basal / Her2 subtypes. Our approach employs a ResNet-50 model for image feature extraction and fully connected layers for gene expression processing, with a cross-attention fusion mechanism to enhance modality interaction. We conduct extensive experiments using five-fold cross-validation, demonstrating that our multimodal integration outperforms unimodal approaches in terms of classification accuracy, precision-recall AUC, and F1-score. Our findings highlight the potential of deep learning for robust and interpretable breast cancer subtype classification, paving the way for improved clinical decision-making.