Baseline Method of the Foundation Model Challenge for Ultrasound Image Analysis

TL;DR

This paper introduces a unified multi-task learning baseline model for ultrasound image analysis, addressing heterogeneity challenges and providing a robust foundation for future foundation models in this domain.

Contribution

It presents the first official baseline for FM_UIA 2026 using a multi-head multi-task learning framework with a shared network architecture.

Findings

Demonstrates the feasibility of a unified model for multiple ultrasound tasks.

Achieves robust performance across diverse subtasks.

Provides publicly available code and dataset for further research.

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}.