Baseline Method of the Foundation Model Challenge for Ultrasound Image Analysis

• URL: http://arxiv.org/abs/2602.01055v1
• Date: Sun, 01 Feb 2026 06:52:11 GMT
• Title: Baseline Method of the Foundation Model Challenge for Ultrasound Image Analysis
• Authors: Bo Deng, Yitong Tang, Jiake Li, Yuxin Huang, Li Wang, Yu Zhang, Yufei Zhan, Hua Lu, Xiaoshen Zhang, Jieyun Bai,
• Abstract summary: We present the Foundation Model Challenge for Ultrasound Image Analysis (FM_UIA2026)<n>The model employs an ImageNet-pretrained EfficientNet--B4 backbone for robust feature extraction, combined with a Feature Pyramid Network (FPN) to capture contextual information.<n>A task-specific routing strategy enables global tasks to leverage high-level semantic features, while dense prediction tasks exploit spatially detailed FPN representations.
• Score: 15.017057362402687
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Ultrasound (US) imaging exhibits substantial heterogeneity across anatomical structures and acquisition protocols, posing significant challenges to the development of generalizable analysis models. Most existing methods are task-specific, limiting their suitability as clinically deployable foundation models. To address this limitation, the Foundation Model Challenge for Ultrasound Image Analysis (FM\_UIA~2026) introduces a large-scale multi-task benchmark comprising 27 subtasks across segmentation, classification, detection, and regression. In this paper, we present the official baseline for FM\_UIA~2026 based on a unified Multi-Head Multi-Task Learning (MH-MTL) framework that supports all tasks within a single shared network. The model employs an ImageNet-pretrained EfficientNet--B4 backbone for robust feature extraction, combined with a Feature Pyramid Network (FPN) to capture multi-scale contextual information. A task-specific routing strategy enables global tasks to leverage high-level semantic features, while dense prediction tasks exploit spatially detailed FPN representations. Training incorporates a composite loss with task-adaptive learning rate scaling and a cosine annealing schedule. Validation results demonstrate the feasibility and robustness of this unified design, establishing a strong and extensible baseline for ultrasound foundation model research. The code and dataset are publicly available at \href{https://github.com/lijiake2408/Foundation-Model-Challenge-for-Ultrasound-Image-Analysis}{GitHub}.

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