发表机构
University of Victoria（维多利亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对3D医学图像分割中基础模型缺乏校准不确定性的问题，提出类别感知非对称加权共形预测（CA-WCP），通过密度比加权和类别特定不对称因子实现覆盖保证，在BraTS 2020和合成CT基准上验证了有效性并生成不确定性条件报告。
AI 中文摘要
可靠的体积分割对于临床诊断至关重要，然而诸如MedSAM等基础模型仍然是确定性的，并且在分布偏移下缺乏校准的不确定性。现有的共形预测方法提供了统计保证，但通常应用于2D场景，并假设对称误差分布，因此无法捕捉3D多类别分割中出现的类别特定偏差。我们提出了类别感知非对称加权共形预测（CA-WCP），该方法将潜空间密度比加权用于协变量偏移，结合方向分位数用于体积下界和上界，并通过验证集假阳性和假阴性率导出的类别特定不对称因子缩放每个边界。我们证明了CA-WCP对每个类别都保留了加权可交换边际覆盖保证，并在3D脑肿瘤分割（BraTS 2020）以及协变量偏移下构建的合成多器官CT基准上进行了评估。在两个基准上，CA-WCP观测覆盖率的95% Clopper-Pearson区间对每个语义类别都包含名义90%水平，而区间宽度相对于对称加权共形预测减少了8-14%。我们进一步将校准区间编码为多模态大语言模型的结构化提示，以生成不确定性条件下的放射学报告，将分布偏移感知的不确定性量化与可解释的临床沟通联系起来。
英文摘要
Reliable volumetric segmentation is critical for clinical diagnostics, yet foundation models such as MedSAM remain deterministic and lack calibrated uncertainty under distribution shift. Existing conformal prediction methods offer statistical guarantees but are frequently applied in 2D and assume symmetric error distributions, so they do not capture the class-specific biases that arise in 3D multi-class segmentation. We propose Class-Aware Asymmetric Weighted Conformal Prediction (CA-WCP), which combines latent-space density-ratio weighting for covariate shift with directional quantiles for the lower and upper volume bounds, and scales each bound by a class-specific asymmetry factor derived from validation-set false-positive and false-negative rates. We prove that CA-WCP retains the weighted-exchangeability marginal coverage guarantee for every class, and we evaluate it on 3D brain tumor segmentation (BraTS 2020) and on a synthetic multi-organ CT benchmark constructed under covariate shift. On both benchmarks the 95\% Clopper--Pearson interval for the observed coverage of CA-WCP contains the nominal 90\% level for every semantic class, while interval width is reduced by 8--14\% relative to symmetric weighted conformal prediction. We further encode the calibrated intervals into structured prompts for a multimodal large language model to produce uncertainty-conditioned radiology reports, linking distribution-shift-aware uncertainty quantification to interpretable clinical communication.
