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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
Dynamic Contamination-Free Medical Benchmark

Dynamic Contamination-Free Medical Benchmark

LiveMedBench offers weekly updated real-world clinical cases for LLM evaluation, avoiding contamination via temporal separation. Multi-agent curation ensures integrity; automated rubric evaluation aligns with experts better than alternatives. Tests reveal top LLMs at 39.2%, highlighting contextual gaps.

ArXiv AIResearchFeb 12#research#livemedbench#v1
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