IECG: Weakly-Supervised Interpretability-Enhancing Foundation Model for Electrocardiogram

Key points

• Electrocardiography (ECG) is a cornerstone of cardiac diagnosis, yet most machine learning methods for ECG analysis lack interpretability, limiting their clinical adoption.1
• Central to IECG is a weakly-supervised attention strategy that explicitly incorporates clinical priors by deriving abnormality heatmap pseudo-labels from established ECG clinical knowledge.1
• During pretraining, IECG is jointly optimized with clinically guided attention supervision, ECG-text contrastive alignment, and report captioning objectives, enabling it to learn rich multimodal representations while producing localized waveform evidence.1
• Extensive experiments demonstrate that IECG achieves state-of-the-art abnormal region localization while maintaining strong classification performance, advancing trustworthy foundation models for ECG analysis.1

Sources (1)

• [1]IECG: Weakly-Supervised Interpretability-Enhancing Foundation Model for Electrocardiogram
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 12:03 PM

  We present IECG, a weakly-supervised ECG foundation model that jointly performs diagnostic classification and abnormality localization without requiring manual abnormal region annotations, overcoming a long-standing bottleneck in interpretable ECG learning.

  Electrocardiography (ECG) is a cornerstone of cardiac diagnosis, yet most machine learning methods for ECG analysis lack interpretability, limiting their clinical adoption.

Extractive summary: sentences quoted from the sources.