用X光片教心电图识别心脏病，让心电图在无X光时也能准判
Contrastive Cross-Modal Learning for Infusing Chest X-ray Knowledge into ECGs
- 通过对比学习对齐心电图与胸片特征，用监督式跨模态损失提升表征能力
- 在三种心脏疾病上均超越基线，水肿诊断最高达78.31 AUROC
- 训练时用胸片知识，推理只靠心电图，适合真实医疗场景部署
现代诊断流程日益多模态，整合医学影像、结构化病历和生理时间序列等数据。其中心电图（ECG）和胸片（CXR）是心脏评估中最常用的两种模态。尽管胸片提供丰富诊断信息，但心电图更易获取，更适合构建可扩展的早期预警系统。本文提出CroMoTEX，一种基于对比学习的新框架，利用胸片训练来学习心电图在多种心脏疾病（心大、胸腔积液、肺水肿）上的临床相关表示。方法采用新型有监督跨模态对比目标，并结合自适应硬负样本加权，实现鲁棒且任务相关的特征学习。测试时仅需心电图输入，可在胸片不可用的真实场景中部署。在大规模MIMIC-IV-ECG和MIMIC-CXR数据集上评估，CroMoTEX在所有三种病理上均优于基线，肺水肿最高达78.31 AUROC。
原文摘要 · Abstract (English)
Modern diagnostic workflows are increasingly multimodal, integrating diverse data sources such as medical images, structured records, and physiological time series. Among these, electrocardiograms (ECGs) and chest X-rays (CXRs) are two of the most widely used modalities for cardiac assessment. While CXRs provide rich diagnostic information, ECGs are more accessible and can support scalable early warning systems. In this work, we propose CroMoTEX, a novel contrastive learning-based framework that leverages chest X-rays during training to learn clinically informative ECG representations for multiple cardiac-related pathologies: cardiomegaly, pleural effusion, and edema. Our method aligns ECG and CXR representations using a novel supervised cross-modal contrastive objective with adaptive hard negative weighting, enabling robust and task-relevant feature learning. At test time, CroMoTEX relies solely on ECG input, allowing scalable deployment in real-world settings where CXRs may be unavailable. Evaluated on the large-scale MIMIC-IV-ECG and MIMIC-CXR datasets, CroMoTEX outperforms baselines across all three pathologies, achieving up to 78.31 AUROC on edema. Our code is available at github.com/vineetpmoorty/cromotex.
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。