PEACE: Cross-modal Enhanced Pediatric-Adult ECG Alignment for Robust Pediatric Diagnosis
This work tackles the problem of scarce pediatric labels for ECG diagnosis by leveraging adult data, but the results are incremental and require further validation.
PEACE addresses the challenge of automated pediatric ECG diagnosis by aligning adult ECG representations with pediatric targets using cross-modal clinical semantic supervision, achieving 59.39% AUC zero-shot and 90.89% AUC full fine-tuning on ZZU-pECG, and 96.65% AUC on PTB-XL.
Automated pediatric electrocardiogram (ECG) diagnosis remains challenging because models trained predominantly on adult data suffer from substantial cross-population mismatch, while pediatric labels are often scarce. We present PEACE (Pediatric-Adult ECG Alignment via Cross-modal Enhancement), a structured cross-modal alignment framework for adult-to-pediatric ECG transfer. PEACE integrates tri-axial clinical semantic decomposition, label-query feature extraction, and curriculum-gated optimization to align transferable adult ECG representations with pediatric diagnostic targets. Since ZZU-pECG provides no paired clinical reports, we generate label-conditioned semantic descriptors using Gemini with concise clinical prompts and use them only as auxiliary training supervision; inference remains ECG-only. On ZZU-pECG, PEACE achieves 59.39%, 79.03%, and 90.89% AUC under zero-shot, 50-shot, and full fine-tuning settings, respectively, and reaches 96.65% AUC on the shared PTB-XL label space. These results suggest that structured clinical semantic supervision can improve low-resource adult-to-pediatric ECG transfer, while prospective clinical validation and more explicit age-aware modeling remain necessary before real-world deployment.