Self-supervised latent prediction on raw complex ultrasound channel data reduces the labeled data needed for sound-speed estimation by roughly 3-4x in simulation, reaching 15.6 m/s error with 10,000 labels.
Forward and inverse scattering in synthetic aperture radar using machine learning,
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IQ-JEPA: A Joint-Embedding Predictive Architecture with a Hermitian Vision Transformer for Sound Speed and Attenuation Estimation from Ultrasound IQ Data
Self-supervised latent prediction on raw complex ultrasound channel data reduces the labeled data needed for sound-speed estimation by roughly 3-4x in simulation, reaching 15.6 m/s error with 10,000 labels.