协调特征条件与频率提示个性化用于多标注者医学分割
Harmonized Feature Conditioning and Frequency-Prompt Personalization for Multi-Rater Medical Segmentation
提出频域提示模块和特征协调网络，将标注者差异建模为风格提示，可推广到其他需要建模标注歧义的视觉任务（如多标注者分类、检测）。
原文依据 We propose a harmonized probabilistic framework that disentangles acquisition artifacts from genuine annotator variability through adaptive feature conditioning and frequency-domain personalization.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论多标注者医学图像分割捕捉了临床解读中固有的歧义性，其中诊断边界因专家和成像设备而异。现有方法通常将这种多样性简化为共识标签或将标注者差异视为噪声，导致模型过度自信且校准不佳。我们提出了一个协调的概率框架，通过自适应特征条件和频域个性化将采集伪影与真实的标注者变异性解耦。一个轻量级的协调器网络隐式地建模扫描仪特定的伪影，并执行动态特征调制以标准化潜变量表示，确保不确定性反映解剖结构而非噪声。为了表示标注者特定的风格，我们引入了高频提示模块，在谱域中操作以编码标注者依赖的边界精度和纹理敏感性。这些提示词自适应地调制协调后的特征，以产生个性化且解剖一致的分割。此外，基于广义能量距离（GED）的正则化将生成分布与经验标注变异性对齐，在专家意见不一致处促进多样性，在专家意见一致处促进共识。在LIDC-IDRI和NPC-170上的实验显示了最先进的聚合和个体化分割，GED显著降低，Dice分数提高，尤其是在噪声病例上。除了准确性，模型表现出具有临床意义的不确定性，置信度在一致区域上升，在模糊区域下降，支持其作为多专家临床工作流中可靠且可解释的工具。
英文原文摘要 1687 字符
Multi-rater medical image segmentation captures the inherent ambiguity of clinical interpretation, where diagnostic boundaries vary across experts and imaging devices. Existing approaches often reduce this diversity to consensus labels or treat rater differences as noise, resulting in overconfident and poorly calibrated models. We propose a harmonized probabilistic framework that disentangles acquisition artifacts from genuine annotator variability through adaptive feature conditioning and frequency-domain personalization. A lightweight Harmonizer Network implicitly models scanner-specific artifacts and performs dynamic feature modulation to standardize latent representations, ensuring that uncertainty reflects anatomy rather than noise. To represent rater-specific styles, we introduce High-Frequency Prompt Modules that operate in the spectral domain to encode annotator-dependent boundary precision and textural sensitivity. These prompts adaptively modulate harmonized features to produce personalized yet anatomically consistent segmentations. Furthermore, a Generalized Energy Distance (GED)-based regularization aligns the generative distribution with empirical annotation variability, promoting diversity where experts disagree and consensus where they converge. Experiments on LIDC-IDRI and NPC-170 show SOTA aggregated and individualized segmentation, with notable GED reductions and improved Dice scores, especially on noisy cases. Beyond accuracy, the model exhibits clinically meaningful uncertainty, confidence rises in agreement regions and declines in ambiguous areas, supporting its use as a reliable and interpretable tool for multi-expert clinical workflows.