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SigLIP Boosts Multi-Label ECG Classification

SigLIP Boosts Multi-Label ECG Classification

Adapts SigLIP contrastive learning with a Jaccard-based sigmoid loss for multi-label ECG classification using real-world data. Incorporates medical knowledge and techniques like higher embedding dimensions and random cropping. Per-label analysis identifies prediction challenges across ECG findings.

ArXiv AIResearchFeb 12#research#siglip-ecg#v1