Abstract
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.
Abstract (translated)
不同设备获取的超声图像风格各异，这会导致下游任务性能下降。为了缩小这种风格差距，未配对图像到图像（UI2I）转换方法旨在将源领域中的图像（即新设备采集的图像）转换为目标领域的图像，在目标领域中已经为下游应用训练好了冻结的任务模型。然而，现有的UI2I方法没有明确地考虑筛选最相关的风格特征，这可能导致转化后的图像与下游任务的需求不一致。 为此，我们提出了TRUST——一种基于令牌驱动的双流框架，该框架在保留源内容的同时转移目标领域的通用风格，并确保内容和风格不会混合。面对目标领域中存在多种风格的情况，我们引入了Token-dRiven（TR）模块，从两个视角进行操作：(1) 数据视角——为每个源标记选择相应的“合适”的目标标记；(2) 模型视角——识别对于下游模型来说的“最佳”目标标记，这由行为镜像损失指导完成。此外，我们还在源编码器中注入了辅助提示，以使内容表示与下游行为相匹配。 在超声数据集上的实验结果表明，TRUST不仅在视觉质量方面优于现有的UI2I方法，在下游任务性能上也同样表现出色。
URL
https://arxiv.org/abs/2509.00508
