AI RESEARCH
Adapting Vision-Language Foundation Model for Next Generation Medical Ultrasound Image Analysis
arXiv CS.CV
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ArXi:2506.08849v4 Announce Type: replace Vision-Language Foundation Models (VLFMs) exhibit remarkable generalization, yet their direct application to medical ultrasound is severely hindered by a profound modality gap. The unique acoustic physics of ultrasound, characterized by speckle noise, shadowing, and heterogeneous textures, often degrades the performance of off-the-shelf VLFMs. To bridge this gap, we propose a novel Hybrid Tuning (HT) strategy for the parameter-efficient adaptation of CLIP-based models to ultrasound analysis.