A Foundational Generative Model for Breast Ultrasound Image Analysis

TL;DR

BUSGen is a pioneering foundational generative model trained on millions of breast ultrasound images, significantly enhancing diagnostic tasks, outperforming radiologists, and enabling privacy-preserving data sharing for breast cancer analysis.

Contribution

The paper introduces BUSGen, the first foundational generative model specifically designed for breast ultrasound analysis, enabling effective data generation and improved diagnostic model performance.

Findings

BUSGen outperformed radiologists in early breast cancer diagnosis.

Generated data was as effective as real data for training models.

BUSGen enhanced the generalization of downstream diagnostic models.

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.