用概率分布建模缺失影像数据的不确定性，提升脑肿瘤分割可靠性。
Set-Inclusive Uncertainty Modeling for Robust Brain Tumor Segmentation
- 将特征表示为高斯分布，均值表任务信息，方差表缺失信息带来的不确定
- 通过对比全模态与部分模态均值差异，动态调节方差大小
- 利用模态子集层次结构和顺序约束，保持不确定性关系一致
多模态MRI对精准脑肿瘤分割至关重要，但实际推理中常无法获取全部模态，导致因信息缺失引发内在不确定性。现有方法将不完整证据编码为确定性表示，虽看似合理却缺乏可靠性。本文提出一种概率表示框架，将表示建模为高斯分布：均值捕捉任务信息，方差衡量缺失证据带来的不确定性。为使方差反映信息不足，我们对每种部分模态配置的均值进行正则化，使其趋近于全模态对应均值，并按对齐后均值的差异比例放大方差。进一步引入集合包含策略，利用模态子集的层次结构并施加顺序约束，以维持不确定性关系的一致性。在BraTS 2018和2020数据集上的大量实验表明，本方法在多种缺失模态场景下均显著优于基线。代码与模型检查点已公开于https://github.com/atlas-sky/SIUM。
原文摘要 · Abstract (English)
Multimodal MRI is essential for accurate brain tumor segmentation. However, acquiring all modalities at inference is often challenging in practice, which causes intrinsic uncertainty due to unavoidable information loss. Without modeling this uncertainty, existing methods encode incomplete evidence into deterministic representations that appear plausible but lack reliability. In this regime, we propose a probabilistic representation framework that models representations as Gaussian distributions, where their mean captures task information and their variance measures uncertainty from missing evidence. To make variance reflect information deficiency, we regularize the mean from each partial configuration toward its full-modality counterpart, while scaling the variance with the discrepancy between their aligned means. We further introduce a set-inclusive strategy that exploits the hierarchical structure of modality subsets and enforces an ordering constraint to maintain their consistent uncertainty relationships. Extensive experiments on BraTS 2018 and 2020 demonstrate that our approach offers superior performance over baselines across diverse missing-modality scenarios. Code and model checkpoint are available at https://github.com/atlas-sky/SIUM.
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