发表机构
School of Instrument Science and Engineering, Southeast University; Nanjing Medical University; Zhengzhou University（东南大学仪器科学与工程学院; 南京医科大学; 郑州大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对成人与儿科心电图转换问题，提出知识引导的跨模态融合框架PEACE，通过标签条件对比对齐等方法，在有限监督下实现更好的儿科心电图解释，消融实验证明标签条件知识对齐是关键驱动因素。
AI 中文摘要
成人和儿科心电图（ECG）解释依赖于年龄敏感标准，当儿科标签稀缺时，主要在成人ECG上预训练的模型向儿科人群的迁移效果往往不佳。现有多模态ECG-文本方法通常在全局样本级别对齐波形和文本，限制了转移。我们提出了通过跨模态增强的儿科-成人ECG对齐（PEACE），这是一个在主要为成人的MIMIC-IV ECG语料库上预训练的知识引导框架。PEACE描述每个诊断的节奏、形态和ST-T轴，并将正标签描述符组合成轴令牌和融合嵌入。标签查询网络（LQN）使用诊断标签作为查询来跨注意力处理ECG令牌和轴令牌，标签集感知双向对比学习（LSBC）在记录共享诊断时将池化的ECG特征与融合嵌入对齐。课程自适应融合（CAF）根据平滑分类损失和训练进度控制对齐强度。知识分支仅用于训练监督，推理仅使用ECG信号。在ZZU-pECG上，PEACE在零样本、50样本和完全微调下的宏平均AUC分别达到59.39%、81.74%和91.56%，在有限监督下比基础和知识预训练基线有最明显的提升。在PTB-XL上微调后，PEACE在九个协调标签上的宏平均AUC达到96.90%。消融实验证实，标签条件知识对齐而非全局文本融合是儿科转移增益的关键驱动因素。
英文摘要
Adult and pediatric electrocardiogram (ECG) interpretation relies on age-sensitive criteria, and models pretrained mainly on adult ECGs often transfer poorly to pediatric populations when pediatric labels are scarce. Existing multimodal ECG--text methods typically align waveforms and text at the global sample level, entangling evidence from co-occurring diagnoses and limiting transfer under this gap. We propose Pediatric-Adult ECG Alignment via Cross-modal Enhancement (PEACE), a knowledge-guided framework pretrained on the largely adult MIMIC-IV ECG corpus. PEACE describes each diagnosis along rhythm, morphology, and ST--T axes and, per recording, composes only positive-label descriptors into three axis tokens and a fused embedding. A label query network (LQN) uses diagnostic labels as queries to cross-attend over ECG tokens and axis tokens, while label set aware bidirectional contrastive learning (LSBC) aligns pooled ECG features with the fused embedding when recordings share diagnoses. Curriculum adaptive fusion (CAF) gates alignment strength according to smoothed classification loss and training progress, limiting disruption during early optimization. The knowledge branch is used only for training supervision; inference uses ECG signals alone. On ZZU-pECG, PEACE reaches macro average AUCs of 59.39%, 81.74%, and 91.56% under zero-shot, 50-shot, and full fine-tuning, with the clearest gains over foundation and knowledge-pretraining baselines under limited supervision; versus domain adaptation initializations, zero-shot improves substantially while 50-shot AUC is comparable to DANN. After fine-tuning on PTB-XL, PEACE reaches 96.90% macro average AUC over nine harmonized labels. Ablations confirm that label-conditioned knowledge alignment, rather than global text fusion, is the key driver of pediatric transfer gains.
CommentsThis article was accidentally submitted as a new arXiv paper instead of a replacement of arXiv:2605.00647. Please refer to arXiv:2605.00647 for the correct and updated version
