URFM: a general Ultrasound Representation Foundation Model for advancing ultrasound image diagnosis

Ultrasound imaging is pivotal in clinical diagnostics, providing critical insights into a wide range of diseases and organs. However, advancing artificial intelligence (AI) in this field is hindered by challenges such as the reliance on large labeled datasets and the limited generalizability of task-specific models, largely due to ultrasound’s low signal-to-noise ratio (SNR). To address these issues, we propose the Ultrasound Representation Foundation Model (URFM), designed to learn robust and generalizable representations from unlabeled ultrasound data.URFM is pre-trained on over 1 million ultrasound images from 15 major anatomical organs, utilizing representation-based masked image modeling (MIM) and state-of-the-art self-supervised learning techniques. Unlike traditional pixel-based MIM, URFM integrates high-level representations from BiomedCLIP, a medical vision-language model, to handle the low SNR inherent in ultrasound imaging. Extensive evaluations show URFM outperforms existing methods, excelling in generalization, label efficiency, and training speed, highlighting its potential to revolutionize diagnostic accuracy and clinical workflows.