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.
Key Points
- 1.Modified sigmoid loss tailored for multi-label ECG
- 2.Integrates medical knowledge in language model
- 3.Mitigates data drift via cropping and scaling
- 4.Improves multimodal medical AI foundations
Impact Analysis
Advances ECG interpretation without specialized equipment, aiding diagnostics in resource-limited settings. Provides framework for ECG-inclusive medical LLMs, enhancing overall healthcare AI.
Technical Details
Employs CLIP-based SigLIP with Jaccard-optimized loss for multi-label prediction. Increases embedding dims and uses random cropping. Analyzes per-label performance on hospital data.