基于注意力机制的原型校准用于多评估者少样本医学图像分割
Attention-Based Prototype Calibration for Multi-Rater Few-Shot Medical Image Segmentation
AI总结：
提出一种注意力原型校准框架，通过建模评估者特异性偏差，在不修改骨干网络的情况下实现个性化分割，有效提升多评估者少样本分割性能。
摘要：
少样本医学图像分割方法通常假设单一真实标注，忽略了临床数据集中常见的不同专家评估者之间的系统性差异。我们提出了一种基于注意力机制的原型校准框架，用于少样本多评估者分割，该框架在原型空间中建模评估者相对于共识表示的特定偏差。一个轻量级且原理性的注意力算子直接优化评估者原型，而不修改骨干特征提取器，使得该方法与现有的基于原型的少样本分割方法完全兼容。这种设计在保持语义一致性的同时，以最小的计算开销实现个性化分割输出。在多评估者医学影像数据集上的实验表明，与基线原型方法相比，该方法持续改进，突出了结构化原型校准在建模标注变异性方面的有效性。我们的代码可在 https://github.com/truong2710-cyber/JAPC 获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
