首页|Unsupervised Test-Time Adaptation for Hepatic Steatosis Grading Using Ultrasound B-Mode Images

Unsupervised Test-Time Adaptation for Hepatic Steatosis Grading Using Ultrasound B-Mode Images

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  • NETL
  • NSTL
  • IEEE
Ultrasound (US) is considered a key modality for the clinical assessment of hepatic steatosis (i.e., fatty liver) due to its noninvasiveness and availability. Deep learning methods have attracted considerable interest in this field, as they are capable of learning patterns in a collection of images and achieve clinically comparable levels of accuracy in steatosis grading. However, variations in patient populations, acquisition protocols, equipment, and operator expertise across clinical sites can introduce domain shifts that reduce model performance when applied outside the original training setting. In response, unsupervised domain adaptation techniques are being investigated to address these shifts, allowing models to generalize more effectively across diverse clinical environments. In this work, we propose a test-time batch normalization (TTN) technique designed to handle domain shift, especially for changes in label distribution, by adapting selected features of batch normalization (BatchNorm) layers in a trained convolutional neural network model. This approach operates in an unsupervised manner, allowing robust adaptation to new distributions without access to label data. The method was evaluated on two abdominal US datasets collected at different institutions, assessing its capability in mitigating domain shift for hepatic steatosis classification. The proposed method reduced the mean absolute error in steatosis grading by 37% and improved the area under the receiver operating characteristic curves (AUC) for steatosis detection from 0.78 to 0.97, compared to nonadapted models. These findings demonstrate the potential of the proposed method to address domain shift in US-based hepatic steatosis diagnosis, minimizing risks associated with deploying trained models in various clinical settings.

Adaptation modelsTrainingData modelsUltrasonic imagingBatch normalizationArtificial intelligenceStandardsAcousticsFrequency controlRobustness

Pedro Vianna、Paria Mehrbod、Muawiz Chaudhary、Michael Eickenberg、Guy Wolf、Eugene Belilovsky、An Tang、Guy Cloutier

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Laboratory of Biorheology and Medical Ultrasonics, Research Center, University of Montreal Hospital, Montreal, QC, Canada|Institute of Biomedical Engineering, University of Montreal, Montreal, QC, Canada

Quebec AI Institute, Mila, Montreal, QC, Canada|Department of Computer Science and Software Engineering, Concordia University, Montreal, QC, Canada

Center for Computational Mathematics, Flatiron Institute, New York, USA

Institute of Biomedical Engineering, University of Montreal, Montreal, QC, Canada|Department of Mathematics and Statistics, University of Montreal, Montreal, QC, Canada

Department of Radiology, Radiation Oncology and Nuclear Medicine, University of Montreal, Montreal, QC, Canada

Laboratory of Biorheology and Medical Ultrasonics, Research Center, University of Montreal Hospital, Montreal, QC, Canada|Institute of Biomedical Engineering, University of Montreal, Montreal, QC, Canada|Department of Radiology, Radiation Oncology and Nuclear Medicine, Institute of Biomedical Engineering, University of Montreal, Montreal, QC, Canada

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2025

10.1109/TUFFC.2025.3555180

IEEE transactions on ultrasonics, ferroelectrics, and frequency control

IEEE transactions on ultrasonics, ferroelectrics, and frequency control

ISSN：
年,卷(期)：2025.72(5)
  • 62