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arXiv AI By Yinghao Xie, Zhenbang Dai, Haojun Wang, Jinyu Cai, Fabio Bonassi, Hongwu Chen, Johan Sundstr\"om, Jiawei Li, Ant\^onio H. Ribeiro

Robust Transfer Learning for Paper ECG Recognition

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RobECG-CL is a rank‑aware contrastive learning framework designed to learn robust representations for paper ECG images, which often suffer from varied layouts, physical artifacts, and limited labels. The method constructs progressively degraded views from standard 12‑lead ECG recordings and trains the model to maintain invariance to the same recording while respecting degradation ordering. In synthetic stress tests on CODE‑II and EchoNext, RobECG‑CL shows improved robustness under severe degradation and few‑shot transfer, outperforming other contrastive baselines and surpassing the waveform‑based foundation model ECG‑FM in a 1% labeled setting; it also achieves the best macro AUROC on 312 hospital samples with 37 labels.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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