Ultrasound (US) imaging poses unique challenges for representation learning due to its inherently noisy acquisition process. The low signal-to-noise ratio and stochastic speckle patterns hinder standard self-supervised learning methods relying on a pixel-level reconstruction objective. Joint-Embedding Predictive Architectures (JEPAs) address this drawback by predicting masked latent representations rather than raw pixels. However, standard approaches depend on hyperparameter-brittle and computationally expensive online teachers updated via exponential moving average. We propose US-JEPA, a self-supervised framework that adopts the Static-teacher Asymmetric Latent Training (SALT) objective. By using a frozen, domain-specific teacher to provide stable latent targets, US-JEPA decouples student-teacher optimization and pushes the student to expand upon the semantic priors of the teacher. In addition, we provide the first rigorous comparison of all publicly available state-of-the-art ultrasound foundation models on UltraBench, a public dataset benchmark spanning multiple organs and pathological conditions. Under linear probing for diverse classification tasks, US-JEPA achieves performance competitive with or superior to domain-specific and universal vision foundation model baselines. Our results demonstrate that masked latent prediction provides a stable and efficient path toward robust ultrasound representations.

翻译：超声成像因其固有的噪声采集过程，给表征学习带来了独特的挑战。低信噪比和随机散斑模式阻碍了依赖像素级重建目标的标准自监督学习方法。联合嵌入预测架构通过预测掩码潜在表征而非原始像素来克服这一缺陷。然而，标准方法依赖于超参数敏感且计算成本高昂的在线教师模型，该模型通过指数移动平均进行更新。我们提出了US-JEPA，这是一个采用静态教师非对称潜在训练目标的自监督框架。通过使用冻结的领域特定教师提供稳定的潜在目标，US-JEPA解耦了学生-教师优化，并推动学生在教师的语义先验基础上进行扩展。此外，我们在UltraBench（一个涵盖多个器官和病理状况的公共数据集基准）上首次对所有公开可用的最先进超声基础模型进行了严格比较。在针对多样化分类任务的线性探测下，US-JEPA实现了与领域特定及通用视觉基础模型基线相当或更优的性能。我们的结果表明，掩码潜在预测为获得稳健的超声表征提供了一条稳定且高效的路径。