用注意力机制校准不同医生的标注差异，提升少样本医学图像分割精度
Attention-Based Prototype Calibration for Multi-Rater Few-Shot Medical Image Segmentation
- 基于注意力机制动态调整每位医生的分割原型，建模个体标注偏差
- 在多医生数据集上显著优于传统原型方法，提升分割一致性
- 轻量设计兼容现有模型，适合临床医学图像少样本分割任务
少样本医学图像分割方法通常假设仅有一个真实标注，忽略了临床数据中常见的多位专家标注间的系统性差异。本文提出一种基于注意力的原型校准框架，用于少样本多标注者分割，通过在原型空间中建模每位标注者相对于共识表示的个体偏差。一种轻量且合理的注意力算子直接优化标注者原型，无需修改主干特征提取器，可完全兼容现有基于原型的少样本分割方法。该设计在保持语义一致性的同时，实现个性化分割输出，计算开销极小。在多个多标注者医学影像数据集上的实验表明，该方法持续优于基线原型方法，验证了结构化原型校准在建模标注变异方面的有效性。
原文摘要 · Abstract (English)
Few-shot medical image segmentation methods typically assume a single ground-truth annotation, overlooking systematic variability across expert raters commonly observed in clinical datasets. We propose an attention-based prototype calibration framework for few-shot multi-rater segmentation that models rater-specific deviations from a consensus representation in prototype space. A lightweight yet principled attention operator directly refines rater prototypes without modifying the backbone feature extractor, making the approach fully compatible with existing prototype-based few-shot segmentation methods. This design preserves semantic consistency while enabling personalized segmentation outputs with minimal computational overhead. Experiments on multi-rater medical imaging datasets demonstrate consistent improvements over baseline prototype approaches, highlighting the effectiveness of structured prototype calibration for modeling annotation variability.
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