Learning from Complementary Ultrasound Representations for Liver Disease Classification

Sabahattin Mert Daloglu; Gokce Bekar; Ceren Coskun; Senanur Sahin; Harvey Castro; Soner Hacihaliloglu; Halley P. Letter; Ilker Hacihaliloglu

arXiv · 2607.12062

Learning from Complementary Ultrasound Representations for Liver Disease Classification

Sabahattin Mert Daloglu·Gokce Bekar·Ceren Coskun·Senanur Sahin·Harvey Castro·Soner Hacihaliloglu·Halley P. Letter·Ilker Hacihaliloglu

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Abstract

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.

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Sabahattin Mert Daloglu, Gokce Bekar, Ceren Coskun, Senanur Sahin, Harvey Castro, Soner Hacihaliloglu, Halley P. Letter, Ilker Hacihaliloglu. 2026-07-13. Learning from Complementary Ultrasound Representations for Liver Disease Classification. https://arxiv.org/abs/2607.12062

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