A Foundational Generative Model for Breast Ultrasound Image Analysis
Journal:
arXiv
Published Date:
Jan 12, 2025
Abstract
Foundational models have emerged as powerful tools for addressing various
tasks in clinical settings. However, their potential development to breast
ultrasound analysis remains untapped. In this paper, we present BUSGen, the
first foundational generative model specifically 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 foundational 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-value<0.0001). Additionally, we
characterized the scaling effect of using generated data which was as effective
as the collected real-world data for training diagnostic models. Moreover,
extensive experiments demonstrated that our approach improved the
generalization ability of downstream models. Importantly, BUSGen protected
patient privacy by enabling fully de-identified data sharing, making progress
forward in secure medical data utilization. An online demo of BUSGen is
available at https://aibus.bio.
