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Ashwath Radhachandran

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#machine learning Preprint Sep 2026

UltraBench 2: Towards Robust Evaluation of Vision Foundation Models on Ultrasound

Benchmarking is an increasingly critical part of research in machine learning and the domains where it is applied, including healthcare. Yet, despite the steady development of new ultrasound foundation models in recent years, the development of well-designed benchmarks to evaluate them has lagged behind. This deficienc...

Ashwath Radhachandran, Adam Tupper, Christian Gagné et al. · 0 citations
#artificial intelligence Preprint Feb 2026

US-JEPA: A Joint Embedding Predictive Architecture for Ultrasound

This work proposes US-JEPA, a self-supervised framework that adopts the Static-teacher Asymmetric Latent Training (SALT) objective and demonstrates that masked latent prediction provides a stable and efficient path toward robust ultrasound representations.

Ashwath Radhachandran, Vedrana Ivezi'c, S. Athreya et al. · 0 citations

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