发表机构
Artikode Intelligence S.L.（Artikode智能有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对条件扩散模型合成超声图像逼真度不足问题，提出无需训练的特征空间候选引导（FSCG）采样策略，通过局部k近邻特征校正及按特征空间能量选最佳候选，缩小与真实图像差距，在多数据集上效果优于标准采样及其他基线。
AI 中文摘要
条件扩散模型可生成解剖学上合理的医学超声（US）图像，但仅解剖学合理性并不能确保逼真的B模式外观。大多数US管道采用标准生成架构并以解剖掩码为条件，或使用强化相同解剖信号的引导机制。然而，B模式US图像受斑点纹理、组织对比度和衰减等采集相关属性影响。利用冻结的US基础模型，发现标准条件扩散基线在表示空间中与真实图像仍有差距。提出特征空间候选引导（FSCG），一种无需训练的采样策略来缩小差距。采样时，FSCG应用局部k近邻特征校正并根据特征空间能量选择最佳随机候选。在三个不同数据集上，FSCG相比标准条件扩散采样，平均FID64降低56%，FID192降低57%，最近邻特征距离降低47%，优于替代推理时间引导基线。结果表明域感知特征表示可揭示并减少医学扩散合成中的逼真度差距而无需重新训练生成器。
英文摘要
Conditional diffusion models can generate anatomically plausible medical ultrasound (US) images, but anatomical plausibility alone does not ensure realistic B-mode appearance. Most US pipelines adapt standard generative architectures and condition them on anatomical masks, or use guidance mechanisms that reinforce the same anatomical signal. However, B-mode US images are shaped by acquisition-dependent properties such as speckle texture, tissue contrast, and attenuation. Using a frozen US foundation model, we show that standard conditional diffusion baselines remain separated from real images in representation space. In this work, we propose Feature-Space Candidate Guidance (FSCG), a training-free sampling strategy to reduce this gap. At sampling time, FSCG applies local k-NN feature correction and selects the best of multiple stochastic candidates according to their feature-space energy. In this way, the mask defines the anatomy, while FSCG steers samples toward the real US domain. Across three different datasets, FSCG reduces average FID64 by 56\%, FID192 by 57\%, and nearest-neighbour feature distance by 47\% over standard conditional diffusion sampling, outperforming alternative inference-time guidance baselines. The results suggest that domain-aware feature representations can reveal and reduce realism gaps in medical diffusion synthesis without retraining the generator. Our code is available at https://github.com/marinadominguez/FSCG.
Comments11 pages, 4 figures. Pre-review manuscript version of a paper accepted at DGM4MICCAI 2026
