MetaCheX融合影像与患者信息，提升胸部X光病灶检测准确率
Enhancing Radiographic Disease Detection with MetaCheX, a Context-Aware Multimodal Model
- 用CNN处理影像+MLP处理患者信息，共享分类器融合多模态数据
- 在CheXpert Plus数据集上AUROC显著提升，整体准确率更高
- 减少算法偏见，适合临床部署的公平性与泛化能力研究
现有深度学习模型在胸片诊断中常忽略患者元数据，限制了诊断准确率与公平性。为此，我们提出MetaCheX，一种新型多模态框架，将胸部X光图像与结构化患者元数据结合，模拟临床决策过程。该方法采用卷积神经网络（CNN）主干网络处理影像，通过多层感知机（MLP）处理元数据，并共享一个分类器进行融合。在CheXpert Plus数据集上的评估显示，无论采用何种CNN架构，MetaCheX均持续优于仅依赖影像的基线模型。引入元数据后，整体诊断准确率显著提升，表现为AUROC提高。研究结果表明，元数据可降低算法偏见，增强模型在多样化人群中的泛化能力。MetaCheX推动临床人工智能向更鲁棒、上下文感知的放射疾病检测迈进。
原文摘要 · Abstract (English)
Existing deep learning models for chest radiology often neglect patient metadata, limiting diagnostic accuracy and fairness. To bridge this gap, we introduce MetaCheX, a novel multimodal framework that integrates chest X-ray images with structured patient metadata to replicate clinical decision-making. Our approach combines a convolutional neural network (CNN) backbone with metadata processed by a multilayer perceptron through a shared classifier. Evaluated on the CheXpert Plus dataset, MetaCheX consistently outperformed radiograph-only baseline models across multiple CNN architectures. By integrating metadata, the overall diagnostic accuracy was significantly improved, measured by an increase in AUROC. The results of this study demonstrate that metadata reduces algorithmic bias and enhances model generalizability across diverse patient populations. MetaCheX advances clinical artificial intelligence toward robust, context-aware radiographic disease detection.
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