融合病理图像与基因数据，提升乳腺癌亚型分类准确率
Multimodal Deep Learning for Subtype Classification in Breast Cancer Using Histopathological Images and Gene Expression Data
- 用双流网络分别处理图像和基因数据，通过交叉注意力融合特征
- 多模态模型在五折交叉验证中准确率、AUC和F1均优于单一模态
- 适合临床研究者与医学人工智能开发者参考
乳腺癌分子亚型分类对个性化治疗和预后评估至关重要。传统方法仅依赖病理图像或基因表达数据，限制了预测能力。本研究提出一种深度多模态学习框架，结合组织病理图像与基因表达数据，将乳腺癌分为BRCA.Luminal与BRCA.Basal/Her2亚型。采用ResNet-50提取图像特征，全连接层处理基因数据，并引入交叉注意力机制增强模态间交互。通过五折交叉验证进行大量实验，结果表明多模态集成在分类准确率、精确率-召回率曲线下面积（AUC）及F1分数上均优于单模态方法。研究证明深度学习可实现鲁棒且可解释的乳腺癌亚型分类，为临床决策提供支持。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。