SuPreME: A Supervised Pre-training Framework for Multimodal ECG Representation Learning (2025.findings-emnlp)
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| Challenge: | Recent ECG Self-Supervised Learning methods mitigate this by learning features without extensive labels but fail to capture fine-grained clinical semantics and require extensive task-specific fine-tuning. |
| Approach: | They propose a supervised pre-training framework for Multimodal ECG representation learning that combines structured diagnostic labels with large language models to help denoise, standardize cardiac concepts and improve clinical representation learning. |
| Outcome: | The proposed framework improves on six downstream datasets covering 106 cardiac conditions and achieves a zero-shot AUC performance of 77.20% over state-of-the-art eSSLs. |
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