# USFM: A Universal Ultrasound Foundation Model Generalized to Tasks and Organs towards Label Efficient Image Analysis

arXiv: https://arxiv.org/abs/2401.00153 · HTML: https://mlsanity.com/paper/2401.00153/

## Key facts

- Cited by 68 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Dec 2023.

## Built on by (most cited first, 30 of 68)

- [A Comprehensive Survey of Foundation Models in Medicine](https://mlsanity.com/paper/2406.10729/) (2024)
- [UltraSam: A Foundation Model for Ultrasound using Large Open-Access Segmentation Datasets](https://mlsanity.com/paper/2411.16222/) (2024)
- [OmniV-Med: Scaling Medical Vision-Language Model for Universal Visual Understanding](https://mlsanity.com/paper/2504.14692/) (2025)
- [EchoFM: Foundation Model for Generalizable Echocardiogram Analysis](https://mlsanity.com/paper/2410.23413/) (2024)
- [From Pretraining to Privacy: Federated Ultrasound Foundation Model with Self-Supervised Learning](https://mlsanity.com/paper/2411.16380/) (2024)
- [EchoWorld: Learning Motion-Aware World Models for Echocardiography Probe Guidance](https://mlsanity.com/paper/2504.13065/) (2025)
- [A Fully Open and Generalizable Foundation Model for Ultrasound Clinical Applications](https://mlsanity.com/paper/2509.11752/) (2025)
- [Improving Representation of High-frequency Components for Medical Visual Foundation Models](https://mlsanity.com/paper/2407.14651/) (2024)
- [Surgical Scene Understanding in the Era of Foundation AI Models: A Comprehensive Review](https://mlsanity.com/paper/2502.14886/) (2025)
- [General Methods Make Great Domain-specific Foundation Models: A Case-study on Fetal Ultrasound](https://mlsanity.com/paper/2506.19552/) (2025)
- [Adapting Vision-Language Foundation Model for Next Generation Medical Ultrasound Image Analysis](https://mlsanity.com/paper/2506.08849/) (2025)
- [OpenMEDLab: An Open-source Platform for Multi-modality Foundation Models in Medicine](https://mlsanity.com/paper/2402.18028/) (2024)
- [ProstNFound+: A Prospective Study using Medical Foundation Models for Prostate Cancer Detection](https://mlsanity.com/paper/2510.26703/) (2025)
- [Self-Supervised Ultrasound-Video Segmentation with Feature Prediction and 3D Localised Loss](https://mlsanity.com/paper/2507.18424/) (2025)
- [UltraSeP: Sequence-aware Pre-training for Echocardiography Probe Movement Guidance](https://mlsanity.com/paper/2408.15026/) (2024)
- [Designing UNICORN: a Unified Benchmark for Imaging in Computational Pathology, Radiology, and Natural Language](https://mlsanity.com/paper/2603.02790/) (2026)
- [Pretext Matters: An Empirical Study of SSL Methods in Medical Imaging](https://mlsanity.com/paper/2603.22649/) (2026)
- [CytoFM: The first cytology foundation model](https://mlsanity.com/paper/2504.13402/) (2025)
- [High Volume Rate 3D Ultrasound Reconstruction with Diffusion Models](https://mlsanity.com/paper/2505.22090/) (2025)
- [DualTrack: Sensorless 3D Ultrasound needs Local and Global Context](https://mlsanity.com/paper/2509.09530/) (2025)
- [US-JEPA: A Joint Embedding Predictive Architecture for Ultrasound](https://mlsanity.com/paper/2602.19322/) (2026)
- [Med-IC: Fusing a Single Layer Involution with Convolutions for Enhanced Medical Image Classification and Segmentation](https://mlsanity.com/paper/2409.18506/) (2024)
- [MambaEviScrib: Mamba and Evidence-Guided Consistency Enhance CNN Robustness for Scribble-Based Weakly Supervised Ultrasound Image Segmentation](https://mlsanity.com/paper/2409.19370/) (2024)
- [Automatic brain tumor segmentation in 2D intra-operative ultrasound images using magnetic resonance imaging tumor annotations](https://mlsanity.com/paper/2411.14017/) (2024)
- [MARMOT: Masked Autoencoder for Modeling Transient Imaging](https://mlsanity.com/paper/2506.08470/) (2025)
- [COME: Dual Structure-Semantic Learning with Collaborative MoE for Universal Lesion Detection Across Heterogeneous Ultrasound Datasets](https://mlsanity.com/paper/2508.09886/) (2025)
- [Semantic Diffusion Posterior Sampling for Cardiac Ultrasound Dehazing](https://mlsanity.com/paper/2508.17326/) (2025)
- [VA-Adapter: Adapting Ultrasound Foundation Model to Echocardiography Probe Guidance](https://mlsanity.com/paper/2510.06809/) (2025)
- [Challenging DINOv3 Foundation Model under Low Inter-Class Variability: A Case Study on Fetal Brain Ultrasound](https://mlsanity.com/paper/2511.01915/) (2025)
- [Automated Interpretable 2D Video Extraction from 3D Echocardiography](https://mlsanity.com/paper/2511.15946/) (2025)

## Abstract (arXiv)

Inadequate generality across different organs and tasks constrains the application of ultrasound (US) image analysis methods in smart healthcare. Building a universal US foundation model holds the potential to address these issues. Nevertheless, the development of such foundational models encounters intrinsic challenges in US analysis, i.e., insufficient databases, low quality, and ineffective features. In this paper, we present a universal US foundation model, named USFM, generalized to diverse tasks and organs towards label efficient US image analysis. First, a large-scale Multi-organ, Multi-center, and Multi-device US database was built, comprehensively containing over two million US images. Organ-balanced sampling was employed for unbiased learning. Then, USFM is self-supervised pre-trained on the sufficient US database. To extract the effective features from low-quality US images, we proposed a spatial-frequency dual masked image modeling method. A productive spatial noise addition-recovery approach was designed to learn meaningful US information robustly, while a novel frequency band-stop masking learning approach was also employed to extract complex, implicit grayscale distribution and textural variations. Extensive experiments were conducted on the various tasks of segmentation, classification, and image enhancement from diverse organs and diseases. Comparisons with representative US image analysis models illustrate the universality and effectiveness of USFM. The label efficiency experiments suggest the USFM obtains robust performance with only 20% annotation, laying the groundwork for the rapid development of US models in clinical practices.