使用组织病理学图像和基因表达数据进行乳腺癌亚型分类的多模态深度学习
Multimodal Deep Learning for Subtype Classification in Breast Cancer Using Histopathological Images and Gene Expression Data
AI总结：
提出整合组织病理学图像与基因表达数据的深度多模态学习框架，采用ResNet-50与交叉注意力机制对乳腺癌亚型进行分类，实验证明其优于单模态方法。
摘要：
乳腺癌的分子亚型分型对个性化治疗和预后至关重要。传统分类方法仅依赖组织病理学图像或基因表达谱，限制了其预测能力。本研究提出一种深度多模态学习框架，整合组织病理学图像与基因表达数据，将乳腺癌分类为BRCA.Luminal和BRCA.Basal/Her2亚型。该方法采用ResNet-50模型提取图像特征，使用全连接层处理基因表达，并引入交叉注意力融合机制增强模态交互。我们通过五折交叉验证进行大量实验，结果表明该多模态整合方法在分类准确率、精确率-召回率AUC和F1分数上均优于单模态方法。研究结果凸显了深度学习在稳健且可解释的乳腺癌亚型分类中的潜力，为改善临床决策铺平了道路。
英文摘要：
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.
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