EchoJEPA通过潜空间预测，让超声心动图模型更抗噪、泛化更强。
EchoJEPA: A Latent Predictive Foundation Model for Echocardiography
- 用潜空间预测目标，自动忽略超声斑点噪声
- 在左室射血分数估计上比基线高20%，右室压估测高17%
- 仅需1%标注数据就达79%准确率，零样本儿科表现超微调模型
超声心动图的基础模型常难以区分解剖信号与固有的斑点噪声和采集伪影。我们提出EchoJEPA，一个在30万患者共1800万张超声心动图上训练的预训练模型，是该模态迄今最大的预训练语料库。通过采用潜空间预测目标，EchoJEPA学习到对斑点噪声不敏感的鲁棒解剖表示。我们使用一种新的多视角探针框架（冻结主干网络）验证其性能，结果表明，在左心室射血分数（LVEF）估计上较领先基线提升约20%，在右心室收缩压（RVSP）估计上提升17%。模型还展现出显著样本效率：仅用1%标注数据即达到79%视图分类准确率，而最佳基线在100%数据下仅达42%。关键的是，其在物理感知声学扰动下的性能下降仅2%，远优于对手的17%。最令人瞩目的是，其在儿科患者上的零样本表现超越全微调基线，确立了潜空间预测作为鲁棒、可泛化医学AI的更优范式。
原文摘要 · Abstract (English)
Foundation models for echocardiography often struggle to disentangle anatomical signal from the stochastic speckle and acquisition artifacts inherent to ultrasound. We present EchoJEPA, a foundation model trained on 18 million echocardiograms across 300K patients, representing the largest pretraining corpus for this modality to date. By leveraging a latent predictive objective, EchoJEPA learns robust anatomical representations that ignore speckle noise. We validate this using a novel multi-view probing framework with frozen backbones, where EchoJEPA outperforms leading baselines by approximately 20% in left ventricular ejection fraction (LVEF) estimation and 17% in right ventricular systolic pressure (RVSP) estimation. The model also exhibits remarkable sample efficiency, reaching 79% view classification accuracy with only 1% of labeled data versus 42% for the best baseline trained on 100%. Crucially, EchoJEPA demonstrates superior generalization, degrading by only 2% under physics-informed acoustic perturbations compared to 17% for competitors. Most remarkably, its zero-shot performance on pediatric patients surpasses fully fine-tuned baselines, establishing latent prediction as a superior paradigm for robust, generalizable medical AI.
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