arXiv Machine Learning July 27, 2026 By Masashi Sode, Gianmarco Pinton

IQ-JEPA: A Joint-Embedding Predictive Architecture with a Hermitian Vision Transformer for Sound Speed and Attenuation Estimation from Ultrasound IQ Data

Read the original on arXiv Machine Learning →

arXiv:2607. 22351v1 Announce Type: new Abstract: The speed of sound in tissue is a prerequisite for well-focused imaging and has diagnostic value, but recovering it from raw pulse-echo channel data is fundamentally a nonlinear inverse problem.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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