VA-Adapter: Adapting Ultrasound Foundation Model to Echocardiography Probe Guidance
This addresses the shortage of skilled personnel in echocardiography, enabling timely heart disease detection, but it is an incremental adaptation of existing foundation models to a specific task.
The paper tackles the problem of high operational difficulty in cardiac ultrasound by adapting an ultrasound foundation model to provide real-time probe guidance for junior sonographers, resulting in a parameter-efficient VA-Adapter that surpasses strong probe guidance models in experiments.
Echocardiography is a critical tool for detecting heart diseases. Recently, ultrasound foundation models have demonstrated remarkable capabilities in cardiac ultrasound image analysis. However, obtaining high-quality ultrasound images is a prerequisite for accurate diagnosis. Due to the exceptionally high operational difficulty of cardiac ultrasound, there is a shortage of highly skilled personnel, which hinders patients from receiving timely examination services. In this paper, we aim to adapt the medical knowledge learned by foundation models from vast datasets to the probe guidance task, which is designed to provide real-time operational recommendations for junior sonographers to acquire high-quality ultrasound images. Moreover, inspired by the practice where experts optimize action decisions based on past explorations, we meticulously design a parameter-efficient Vision-Action Adapter (VA-Adapter) to enable foundation model's image encoder to encode vision-action sequences, thereby enhancing guidance performance. With built-in sequential reasoning capabilities in a compact design, the VA-Adapter enables a pre-trained ultrasound foundation model to learn precise probe adjustment strategies by fine-tuning only a small subset of parameters. Extensive experiments demonstrate that the VA-Adapter can surpass strong probe guidance models. Our code will be released after acceptance.