用随机游走增强医学图像分割的不确定性估计，让结果更符合解剖结构。
Anatomically-aware conformal prediction for medical image segmentation with random walks
- 基于视觉基础模型特征构建邻近图，通过随机游走扩散不确定性。
- 在误差率10%下，分割质量提升最高达35.4%，边界更连续。
- 适用于任何分割模型，提升临床可用性，适合医疗部署场景。
深度学习在医学影像中的可靠应用需要提供严格误差保证且具解剖意义的不确定性量化。共形预测（CP）是一种强大的无分布框架，可构建统计有效的预测区间。然而，标准分割中的应用常忽略解剖上下文，导致预测集碎片化、空间不连贯且过度分割，限制了临床价值。本文提出随机游走共形预测（RW-CP），一种可添加于任意分割方法之上的模型无关框架。RW-CP通过构建预训练视觉基础模型特征的k近邻图，并施加随机游走以扩散不确定性，使非一致性得分更平滑，降低对共形校准参数λ的敏感性，从而实现更稳定、更连续的解剖边界。该方法保持严格的边际覆盖性，显著提升分割质量。在多模态公开数据集上的评估显示，在允许误差率α=0.1的条件下，相比标准CP基线，性能提升最高达35.4%。
原文摘要 · Abstract (English)
The reliable deployment of deep learning in medical imaging requires uncertainty quantification that provides rigorous error guarantees while remaining anatomically meaningful. Conformal prediction (CP) is a powerful distribution-free framework for constructing statistically valid prediction intervals. However, standard applications in segmentation often ignore anatomical context, resulting in fragmented, spatially incoherent, and over-segmented prediction sets that limit clinical utility. To bridge this gap, this paper proposes Random-Walk Conformal Prediction (RW-CP), a model-agnostic framework which can be added on top of any segmentation method. RW-CP enforces spatial coherence to generate anatomically valid sets. Our method constructs a k-nearest neighbour graph from pre-trained vision foundation model features and applies a random walk to diffuse uncertainty. The random walk diffusion regularizes the non-conformity scores, making the prediction sets less sensitive to the conformal calibration parameter $λ$, ensuring more stable and continuous anatomical boundaries. RW-CP maintains rigorous marginal coverage while significantly improving segmentation quality. Evaluations on multi-modal public datasets show improvements of up to $35.4\%$ compared to standard CP baselines, given an allowable error rate of $α=0.1$.
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