URFM: A general Ultrasound Representation Foundation Model for advancing ultrasound image diagnosis.
Authors
Affiliations (8)
Affiliations (8)
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China.
- Med-X Center for Informatics, Sichuan University, Chengdu, Sichuan 610041, China.
- West China Hospital-SenseTime Joint Lab, Chengdu, Sichuan 610041, China.
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China.
- Stork Healthcare, Chengdu 610041, Sichuan, China.
- Department of Ultrasonography, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China.
- School of Biomedical Engineering, Tsinghua University, Beijing 100084, China.
- Sichuan University - Pittsburgh Institute, Sichuan University, Chengdu, Sichuan 610207, China.
Abstract
Ultrasound imaging is critical for clinical diagnostics, providing insights into various diseases and organs. However, artificial intelligence (AI) in this field faces challenges, such as the need for large labeled datasets and limited task-specific model applicability, particularly due to ultrasound's low signal-to-noise ratio (SNR). To overcome these, we introduce the Ultrasound Representation Foundation Model (URFM), designed to learn robust, generalizable representations from unlabeled ultrasound images, enabling label-efficient adaptation to diverse diagnostic tasks. URFM is pre-trained on over 1M images from 15 major anatomical organs using representation-based masked image modeling (MIM), an advanced self-supervised learning. Unlike traditional pixel-based MIM, URFM integrates high-level representations from BiomedCLIP, a specialized medical vision-language model, to address the low SNR issue. Extensive evaluation shows that URFM outperforms state-of-the-art methods, offering enhanced generalization, label efficiency, and training-time efficiency. URFM's scalability and flexibility signal a significant advancement in diagnostic accuracy and clinical workflow optimization in ultrasound imaging.