解决超声图像设备差异导致的诊断不准问题，提升跨设备诊断效果。

The appearance of ultrasound images varies across acquisition devices, causing domain shifts that degrade the performance of fixed black-box downstream inference models when reused. To mitigate this issue, it is practical to develop unpaired image translation (UIT) methods that effectively align the statistical distributions between source and target domains, particularly under the constraint of a reused inference-blackbox setting. However, existing UIT approaches often overlook class-specific semantic alignment during domain adaptation, resulting in misaligned content-class mappings that can impair diagnostic accuracy. To address this limitation, we propose UI-Styler, a novel ultrasound-specific, class-aware image style transfer framework. UI-Styler leverages a pattern-matching mechanism to transfer texture patterns embedded in the target images onto source images while preserving the source structural content. In addition, we introduce a class-aware prompting strategy guided by pseudo labels of the target domain, which enforces accurate semantic alignment with diagnostic categories. Extensive experiments on ultrasound cross-device tasks demonstrate that UI-Styler consistently outperforms existing UIT methods, achieving state-of-the-art performance in distribution distance and downstream tasks, such as classification and segmentation.