回声桥：用于超声心动图衍生心脏发现的长尾感知心电图-超声心动图文本对齐

中文摘要 AI 辅助

标准化的超声心动图结论为学习超声心动图衍生心脏发现的心电图表示提供了有意义的监督。全局心电图-文本对齐可能会混淆特定模态因素，而长尾发现分布为低流行情况提供了稀疏的正监督。我们提出了具有互补共享-私有投影（CSPP）和自适应原型边界校准（APBC）的回声桥。CSPP将每个模态映射到共享和辅助私有投影中，通过模态内正交性减少方向冗余，并双向对齐归一化的共享投影。APBC利用特定类别的原型、训练频率自适应角度边界和球形里斯排斥来组织共享超球面。我们在EchoNext-Mini以及独立的PKUPH和SHTMU队列上，在四种协议下评估回声桥：无需下游分类器训练基于提示推理、域内冻结线性探测、目标域跨中心冻结线性探测和仅源跨中心转移，并辅以特定发现分析。回声桥在最强基线的基础上，分别将无分类器的AUROC、AUPRC和F1提高了7.88分、5.61分和4.54分，并在所有域内探测预算、目标域探测预算和两个仅源转移队列中取得了最高的点估计值。特定发现分析表明，在大多数情况下都有增益，包括几种低流行率的瓣膜发现。

英文摘要

Standardized echocardiography conclusions provide meaningful supervision for learning ECG representations of echocardiography-derived cardiac findings. Global ECG--text alignment may entangle modality-specific factors, while long-tailed finding distributions provide sparse positive supervision for low-prevalence conditions. We propose EchoBridge with Complementary Shared--Private Projection (CSPP) and Adaptive Prototype Boundary Calibration (APBC). CSPP maps each modality into shared and auxiliary private projections, reduces directional redundancy via within-modality orthogonality, and bidirectionally aligns normalized shared projections. APBC organizes the shared hypersphere with class-specific prototypes, training-frequency-adaptive angular margins, and spherical Riesz repulsion. We evaluate EchoBridge on EchoNext-Mini and independent PKUPH and SHTMU cohorts under four protocols: prompt-based inference without downstream classifier training, in-domain frozen linear probing, target-domain cross-center frozen linear probing, and source-only cross-center transfer, supplemented by finding-specific analyses. EchoBridge improves classifier-free AUROC, AUPRC, and F1 over the strongest baselines by 7.88, 5.61, and 4.54 points, respectively, and achieves the highest point estimates across all in-domain and target-domain probing budgets and both source-only transfer cohorts. Finding-specific analyses show gains for most conditions, including several low-prevalence valvular findings.

发表机构

• School of Intelligence Science and Technology, Peking University（北京大学智能科学与技术学院）
• University of the Chinese Academy of Sciences（中国科学院大学）
• State Key Laboratory of General Artificial Intelligence, Peking University（北京大学通用人工智能重点实验室）
• National Institute of Health Data Science, Peking University（北京大学健康数据科学研究所）
• Department of Cardiology, Peking University People’s Hospital（北京大学人民医院心内科）

机构由 AI 辅助整理，请以论文原文为准。