发表机构
PONS Incorporated; Mayo Clinic; University of British Columbia（庞斯公司; 梅奥诊所; 英属哥伦比亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究利用超声区分NASH和NAFLD的挑战，结合传统B模式超声与其他表征，用自监督掩码自动编码器和图卷积网络评估，在多站点队列实验中，互补超声表征提升了分类性能，各方面均有性能改善。
AI 中文摘要
由于细微的组织变化和传统B模式成像中可用信息有限，使用超声区分非酒精性脂肪性肝炎（NASH）和非酒精性脂肪肝（NAFLD）仍然具有挑战性。在这项工作中，我们研究了从同一采集获得的互补超声表征是否能改善NASH与NAFLD的分类。具体而言，我们将传统B模式超声与基于物理引导和局部相位的图像表征相结合，并使用自监督掩码自动编码器（MAE）和图卷积网络（GCN）评估其有效性。实验在梅奥诊所的一个多站点队列上进行，该队列由125名患者的2547次肝脏超声扫描组成。与单独使用传统B模式超声相比，互补超声表征持续提高了分类性能，准确率提高了32.4%，F1分数提高了91.2%。此外，在年龄组、性别、种族、民族和采集地点中都持续观察到了性能提升。
英文摘要
Differentiating non-alcoholic steatohepatitis (NASH) from non-alcoholic fatty liver disease (NAFLD) using ultrasound remains challenging due to subtle tissue alterations and the limited information available in conventional B-mode imaging. In this work, we investigate whether complementary ultrasound representations derived from the same acquisition can improve NASH versus NAFLD classification. Specifically, we combine conventional B-mode ultrasound with physics-guided and local phase-based image representations and evaluate their effectiveness using self-supervised masked autoencoders (MAEs) and graph convolutional networks (GCNs). Experiments were conducted on a multi-site Mayo Clinic cohort consisting of 2,547 liver ultrasound scans from 125 patients. Compared with conventional B-mode ultrasound alone, complementary ultrasound representations consistently improved classification performance, yielding gains of up to 32.4% in accuracy and 91.2% in F1-score. Furthermore, performance improvements were consistently observed across age groups, sex, race, ethnicity,and acquisition sites.
CommentsSubmitted to the MICCAI 2026 ASMUS Workshop (under review)
