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<p>Foundation models have emerged as powerful tools for addressing various tasks in clinical settings. However, their potential development for breast ultrasound analysis remains untapped. Here we present BUSGen, the first foundation generative model designed for breast ultrasound image analysis. Pretrained on over 3.5 million breast ultrasound images, BUSGen has acquired extensive knowledge of breast structures, pathological features and clinical variations. With few-shot adaptation, BUSGen can generate repositories of realistic and informative task-specific data, facilitating the development of models for a wide range of downstream tasks. Extensive experiments highlight BUSGen’s exceptional adaptability, significantly exceeding real-data-trained foundation models in breast cancer screening, diagnosis and prognosis. In breast cancer early diagnosis, our approach outperformed all board-certified radiologists (<i>n</i> = 9), achieving an average sensitivity improvement of 16.5% (<i>P</i> &lt; 0.0001). In addition, we characterized the scaling effect of using synthetic data. Finally, BUSGen enabled de-identified data sharing, making progress forward in secure medical data utilization.</p>

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A foundation generative model for breast ultrasound image analysis

  • 期刊论文

  • 发表日期：

    2026年4月7日

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  • Haojun Yu,
  • Youcheng Li,
  • Nan Zhang,
  • Zihan Niu,
  • Xuantong Gong,
  • Yanwen Luo,
  • Haotian Ye,
  • Siyu He,
  • Quanlin Wu,
  • Wangyan Qin,
  • Mengyuan Zhou,
  • Jie Han,
  • Jia Tao,
  • Ziwei Zhao,
  • Di Dai,
  • Di He,
  • Dong Wang,
  • Binghui Tang,
  • Ling Huo,
  • James Zou,
  • Qingli Zhu,
  • Yong Wang,
  • Liwei Wang

摘要

Foundation models have emerged as powerful tools for addressing various tasks in clinical settings. However, their potential development for breast ultrasound analysis remains untapped. Here we present BUSGen, the first foundation generative model designed for breast ultrasound image analysis. Pretrained on over 3.5 million breast ultrasound images, BUSGen has acquired extensive knowledge of breast structures, pathological features and clinical variations. With few-shot adaptation, BUSGen can generate repositories of realistic and informative task-specific data, facilitating the development of models for a wide range of downstream tasks. Extensive experiments highlight BUSGen’s exceptional adaptability, significantly exceeding real-data-trained foundation models in breast cancer screening, diagnosis and prognosis. In breast cancer early diagnosis, our approach outperformed all board-certified radiologists (n = 9), achieving an average sensitivity improvement of 16.5% (P < 0.0001). In addition, we characterized the scaling effect of using synthetic data. Finally, BUSGen enabled de-identified data sharing, making progress forward in secure medical data utilization.