用多维度知识蒸馏，让小模型也能高效精准识读心电图。
EVL-ECG: Efficient ECG Interpretation With Multi-Aspect Heterogeneous Knowledge Distillation
- 通过跨架构注意力对齐，保留心电图细微波形特征。
- 引入最优传输匹配，保持多导联间全局结构关系。
- 蒸馏教师模型的诊断逻辑，适合边缘医疗场景使用。
高保真心电图解读日益依赖大规模基础模型，但其在临床边缘计算环境中的部署受限于极高的计算开销。尽管知识蒸馏（KD）是潜在解决方案，传统方法在跨异构架构迁移时难以捕捉心电图复杂的时空依赖性。本文提出EVL-ECG框架，专为心脏诊断逻辑的跨架构蒸馏设计。引入三项心电图感知创新：(1) 多头交叉注意力对齐，协调架构差异以保留细粒度形态特征；(2) 基于最优传输的视觉特征匹配，利用最优传输维持多导联间全局结构关系，克服令牌表示不一致问题；(3) 几何内架构关系匹配，蒸馏教师模型的隐含诊断推理。在多个心电图基准上评估显示，EVL-ECG相较现有基线提升最高达2.4% AUC与1.1%临床准确率。尤为关键的是，该框架构建了适用于资源受限临床环境的20亿参数高效心电图基础模型。
原文摘要 · Abstract (English)
High-fidelity ECG interpretation is increasingly reliant on massive foundation models, yet their deployment in clinical edge-care remains hindered by extreme computational demands. While knowledge distillation (KD) is a promising solution, traditional methods fail to capture the complex spatio-temporal dependencies of ECG signals when transferring knowledge across heterogeneous architectures. In this paper, we propose EVL-ECG, a framework specifically designed for cross-architecture distillation of cardiac diagnostic logic. EVL-ECG introduces three ECG-aware innovations: (1) Multi-Head Cross-Attention Alignment, which harmonizes architectural discrepancies to preserve fine-grained morphological features; (2) Optimal Transport-based Visual Feature Matching, utilizing optimal transport to maintain global structural relationships across ECG leads despite mismatched token representations; and (3) Geometric Intra-Architecture Relation Matching, which distills the latent diagnostic reasoning of the teacher model. Evaluations across ECG benchmarks demonstrate that EVL-ECG yields improvements of up to 2.4% AUC and 1.1% clinical accuracy over existing baselines. Notably, EVL-ECG establishes an efficient 2B-parameter ECG foundation model, suitable for resource-constrained clinical environments.
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