Anatomy-Anchored Self-Supervision: Distilling Vision Foundation Models for Invariant Ultrasound Representation
arXiv:2605.25402v2 Announce Type: replace-cross Abstract: Self-supervised pre-training paradigm has gained increasing prominence for learning transferable representations in medical imaging, yet existing methods for ultrasound (US) images operate at the image or frame level, overlooking the anatomical context for clinical-aligned representation learning. In this work, we propose an anatomy-anchored ultrasound self-supervision framework ANAUS that shifts representation learning from generic visual regions to clinically meaningful anatomical structures. Utilizing a learnable latent prompt engine
The proliferation of self-supervised learning in AI combined with the increasing need for robust medical imaging interpretation makes this advancement timely.
This development allows AI models to interpret medical images, specifically ultrasounds, with greater clinical relevance by focusing on anatomical structures rather than generic visual features, promising more accurate and useful AI diagnostics.
AI models can now learn more contextually relevant representations from medical images, reducing the need for extensive manual annotation and improving their applicability in clinical settings.
- · Medical AI developers
- · Radiology departments
- · Healthcare diagnostics
- · Patients
- · Traditional image processing methods
- · Generic self-supervised learning models in medical imaging
Improved accuracy and efficiency in AI-assisted ultrasound diagnostics.
Faster and more reliable detection of medical conditions, potentially leading to earlier intervention and better patient outcomes.
Reduced reliance on highly specialized human expertise for initial screenings, democratizing access to advanced medical diagnostics.
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Read at arXiv cs.AI