Fine-grained Contrastive Learning for ECG-Report Alignment with Waveform Enhancement
This work addresses a domain-specific problem for medical AI by enhancing ECG diagnosis through fine-grained alignment, though it is incremental as it builds on existing contrastive learning methods.
The paper tackles the problem of aligning local ECG features with individual report tags in ECG-report contrastive learning, achieving significant improvements in fine-grained alignment and outperforming state-of-the-art methods in zero-shot prediction and linear probing on six datasets.
Electrocardiograms (ECGs) are essential for diagnosing cardiovascular diseases. However, existing ECG-Report contrastive learning methods focus on whole-ECG and report alignment, missing the link between local ECG features and individual report tags. In this paper, we propose FG-CLEP (Fine-Grained Contrastive Language ECG Pre-training), which achieves fine-grained alignment between specific ECG segments and each tag in the report via tag-specific ECG representations. Furthermore, we found that nearly 55\% of ECG reports in the MIMIC-ECG training dataset lack detailed waveform features, which hinders fine-grained alignment. To address this, we introduce a coarse-to-fine training process that leverages large language models (LLMs) to recover these missing waveform features and validate the LLM outputs using a coarse model. Additionally, fine-grained alignment at the tag level, rather than at the report level, exacerbates the false negative problem, as different reports may share common tags. To mitigate this, we introduce a semantic similarity matrix to guide the model in identifying and correcting false negatives. Experiments on six datasets demonstrate that FG-CLEP significantly improves fine-grained alignment, outperforming state-of-the-art methods in both zero-shot prediction and linear probing. Meanwhile, the fine-grained reports we generate also enhance the performance of other methods.