arXiv Artificial Intelligence

Robust Transfer Learning for Paper ECG Recognition

1 hour ago 01.10.2026 07:00 👁 1 views
Robust Transfer Learning for Paper ECG Recognition

Quick summary

arXiv:2609.39581v1 Announce Type: cross 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 se

Key takeaways

  • arXiv:2609.39581v1 Announce Type: cross 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.

Why it matters

“Robust Transfer Learning for Paper ECG Recognition” highlights the need for repeatable measurement rather than a single impressive demonstration. Independent validation across datasets and clearly stated limitations determine whether a result can guide product decisions.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗