针对超声设备间图像风格差异，提出基于令牌驱动的风格迁移方法，提升下游任务性能。
TRUST: Token-dRiven Ultrasound Style Transfer for Cross-Device Adaptation
- 通过双流框架分离内容与风格，仅迁移目标设备共性风格。
- 引入令牌驱动模块，精准选择适配源图的风格特征。
- 在多个超声数据集上显著优于现有方法，适合医学影像跨设备应用。
不同设备获取的超声图像风格差异大，影响下游任务性能。现有无配对图像到图像翻译方法未显式筛选关键风格特征，导致生成图像与下游任务需求不匹配。本文提出TRUST，一种令牌驱动的双流框架，在保留源图像内容的同时，迁移目标域的通用风格，确保内容与风格不混淆。针对目标域中多种风格，设计令牌驱动（TR）模块，从数据视角选择与源令牌对应的合适目标令牌，从模型视角通过行为镜像损失识别对下游模型最优的目标令牌。同时，向源编码器注入辅助提示，使内容表征与下游行为对齐。在多个超声数据集上的实验表明，TRUST在视觉质量和下游任务性能上均优于现有方法。
原文摘要 · Abstract (English)
Ultrasound images acquired from different devices exhibit diverse styles, resulting in decreased performance of downstream tasks. To mitigate the style gap, unpaired image-to-image (UI2I) translation methods aim to transfer images from a source domain, corresponding to new device acquisitions, to a target domain where a frozen task model has been trained for downstream applications. However, existing UI2I methods have not explicitly considered filtering the most relevant style features, which may result in translated images misaligned with the needs of downstream tasks. In this work, we propose TRUST, a token-driven dual-stream framework that preserves source content while transferring the common style of the target domain, ensuring that content and style remain unblended. Given multiple styles in the target domain, we introduce a Token-dRiven (TR) module that operates from two perspectives: (1) a data view--selecting "suitable" target tokens corresponding to each source token, and (2) a model view--identifying ``optimal" target tokens for the downstream model, guided by a behavior mirror loss. Additionally, we inject auxiliary prompts into the source encoder to match content representation with downstream behavior. Experimental results on ultrasound datasets demonstrate that TRUST outperforms existing UI2I methods in both visual quality and downstream task performance.
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