US-JEPA: A Joint Embedding Predictive Architecture for Medical Ultrasound

• URL: http://arxiv.org/abs/2602.19322v1
• Date: Sun, 22 Feb 2026 19:56:56 GMT
• Title: US-JEPA: A Joint Embedding Predictive Architecture for Medical Ultrasound
• Authors: Ashwath Radhachandran, Vedrana Ivezić, Shreeram Athreya, Ronit Anilkumar, Corey W. Arnold, William Speier,
• Abstract summary: Ultrasound (US) imaging poses unique challenges for representation learning due to its inherently noisy acquisition process.<n>Joint-Embedding Predictive Architectures (JEPAs) predict masked latent representations rather than raw pixels.<n>We propose US-JEPA, a self-supervised framework that adopts the Static-teacher Asymmetric Latent Training (SALT) objective.
• Score: 5.216814358105614
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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