发表机构
Uppsala University; Nanjing Medical University; The First Affiliated Hospital with Nanjing Medical University（乌普萨拉大学; 南京医科大学; 南京医科大学第一附属医院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出RobECG-CL秩感知对比学习框架，通过逐步退化视图训练，提升纸质心电图在严重退化与少样本迁移下的鲁棒性，并在医院数据上取得最佳宏AUROC。
AI 中文摘要
纸质心电图识别具有挑战性，因为现实世界中的心电图图像在布局、物理伪影和标签可用性方面存在差异。我们提出了RobECG-CL，一种用于鲁棒纸质心电图表示学习的秩感知对比学习框架。从标准的12导联心电图记录出发，我们构建了具有异构布局的逐步退化的纸质心电图视图，并训练模型以平衡同一记录的不变性与退化感知排序。在CODE-II和EchoNext上的综合压力测试中，RobECG-CL在严重退化和少样本迁移下提高了鲁棒性，优于对比学习基线，并在1%标注设置下超越了基于波形的基座模型ECG-FM。在包含37个标签的312个医院数据样本上，RobECG-CL取得了最佳的宏AUROC。
英文摘要
Paper ECG recognition is challenging because real-world ECG images vary in layout, physical artifacts, and label availability. We introduce RobECG-CL, a rank-aware contrastive learning framework for robust paper ECG representation learning. Starting from standard 12-lead ECG recordings, we construct progressively degraded paper ECG views with heterogeneous layouts and train the model to balance same-recording invariance with degradation-aware ordering. Across synthetic stress tests on CODE-II and EchoNext, RobECG-CL improves robustness under severe degradation and few-shot transfer, outperforming contrastive learning baselines and surpassing the waveform-based foundation model, ECG-FM, in the 1% labeled setting. On 312 samples of hospital data with 37 labels, RobECG-CL achieves the best macro AUROC.
