标题： 以半监督知识蒸馏方法实现低成本心脏智能 显示英文标题

标题： Approaching Low-Cost Cardiac Intelligence with Semi-Supervised Knowledge Distillation

摘要： 将先进的心脏人工智能应用于日常心脏监测受到其对大量医疗数据和高计算资源的依赖的阻碍。 低成本心脏智能（LCCI）通过使用可穿戴设备数据（如1导联心电图（ECG））提供了一个有前景的替代方案，但与高成本心脏智能（HCCI）相比，其诊断性能存在显著差距。 为了弥合这一差距，我们提出了LiteHeart，这是一种半监督知识蒸馏框架。 LiteHeart引入了一个区域感知的知识蒸馏模块，以模仿心脏病专家如何关注具有诊断意义的ECG区域，并引入了一个跨层互信息模块，以对齐LCCI和HCCI系统的决策过程。 采用半监督训练策略，LiteHeart进一步提高了在有限监督下的模型鲁棒性。 在涵盖超过38种心血管疾病的五个数据集上进行评估，LiteHeart显著缩小了LCCI和HCCI之间的性能差距，在宏F1分数上比现有方法提高了4.27%到7.10%。 这些结果表明，LiteHeart显著增强了低成本心脏智能系统的诊断能力，为利用可穿戴技术实现可扩展、负担得起且准确的日常心脏健康护理铺平了道路。 显示英文摘要

摘要： Deploying advanced cardiac artificial intelligence for daily cardiac monitoring is hindered by its reliance on extensive medical data and high computational resources. Low-cost cardiac intelligence (LCCI) offers a promising alternative by using wearable device data, such as 1-lead electrocardiogram (ECG), but it suffers from a significant diagnostic performance gap compared to high-cost cardiac intelligence (HCCI). To bridge this gap, we propose LiteHeart, a semi-supervised knowledge distillation framework. LiteHeart introduces a region-aware distillation module to mimic how cardiologists focus on diagnostically relevant ECG regions and a cross-layer mutual information module to align the decision processes of LCCI and HCCI systems. Using a semi-supervised training strategy, LiteHeart further improves model robustness under limited supervision. Evaluated on five datasets covering over 38 cardiovascular diseases, LiteHeart substantially reduces the performance gap between LCCI and HCCI, outperforming existing methods by 4.27% to 7.10% in macro F1 score. These results demonstrate that LiteHeart significantly enhances the diagnostic capabilities of low-cost cardiac intelligence systems, paving the way for scalable, affordable, and accurate daily cardiac healthcare using wearable technologies.