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arXiv · 2609.39581

Robust Transfer Learning for Paper ECG Recognition

Yinghao Xie·Zhenbang Dai·Haojun Wang·Jinyu Cai·Fabio Bonassi·Hongwu Chen·Johan Sundström·Jiawei Li·Antônio H. Ribeiro
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Abstract

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.

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Yinghao Xie, Zhenbang Dai, Haojun Wang, Jinyu Cai, Fabio Bonassi, Hongwu Chen, Johan Sundström, Jiawei Li, Antônio H. Ribeiro. 2026-09-30. Robust Transfer Learning for Paper ECG Recognition. https://arxiv.org/abs/2609.39581

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