Adapting Vision-Language Foundation Model for Next Generation Medical Ultrasound Image Analysis

Vision-Language Models (VLMs) have demonstrated remarkable generalization capabilities, yet their application to medical ultrasound remains constrained by the significant domain shift between natural images and sonographic data. The unique physics of ultrasound, manifesting as speckle noise, shadowing, and variable artifacts, often leads to suboptimal performance when applying off-the-shelf foundation models. To address this, we propose a novel Hybrid-tuning (HT) strategy for the efficient adaptation of CLIP-based models to ultrasound analysis. Our method introduces a lightweight adapter module integrated into the frozen visual backbone, featuring frequency-domain filtering to suppress periodic artifacts and dynamic noise estimation to calibrate feature representations. Furthermore, we design specialized segmentation and classification heads that employ multi-scale feature aggregation to maximize the utility of pre-trained semantic priors. Extensive evaluations across six multi-center datasets (covering lymph nodes, breast, thyroid, and prostate) reveal that our HT-enhanced models significantly outperform existing state-of-the-art methods, including BiomedCLIP and standard LoRA fine-tuning. The results highlight the superior data efficiency and robustness of our approach, paving the way for practical, foundational intelligence in automated ultrasound diagnosis. The source code is available atthis https URL.