利用专家分歧模拟临床会诊，提升医学图像分割鲁棒性
Learning from Disagreement: A Group Decision Simulation Framework for Robust Medical Image Segmentation
- 通过模拟临床专家组决策过程，建模不同标注者风格
- 在CBCT和MRI数据上分别取得92.11%和90.72%的Dice分数
- 适合需要高可信度医疗AI的临床场景研究者
医学图像分割标注因标注者专业水平差异及图像固有模糊性，普遍存在评价者间变异（IRV）。传统平均专家标签的方法会丢弃分歧中蕴含的临床不确定性。本文提出群体决策模拟框架，通过专家签名生成器（ESG）在独特隐空间中学习个体标注风格，并由模拟会诊模块（SCM）从该空间采样生成最终分割结果。该方法在具有挑战性的CBCT和MRI数据集上分别达到92.11%和90.72%的Dice分数，显著优于现有方法。通过将专家分歧视为有效信号而非噪声，为构建更鲁棒、可信赖的医疗AI系统提供了清晰路径。
原文摘要 · Abstract (English)
Medical image segmentation annotation suffers from inter-rater variability (IRV) due to differences in annotators' expertise and the inherent blurriness of medical images. Standard approaches that simply average expert labels are flawed, as they discard the valuable clinical uncertainty revealed in disagreements. We introduce a fundamentally new approach with our group decision simulation framework, which works by mimicking the collaborative decision-making process of a clinical panel. Under this framework, an Expert Signature Generator (ESG) learns to represent individual annotator styles in a unique latent space. A Simulated Consultation Module (SCM) then intelligently generates the final segmentation by sampling from this space. This method achieved state-of-the-art results on challenging CBCT and MRI datasets (92.11% and 90.72% Dice scores). By treating expert disagreement as a useful signal instead of noise, our work provides a clear path toward more robust and trustworthy AI systems for healthcare.
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