面向可靠脑肿瘤分割的缺失模态感知校准
Missing Modality-Aware Calibration for Trustworthy Brain Tumor Segmentation
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中文总结 AI 辅助
针对脑肿瘤分割中缺失模态导致的校准误差问题，提出MMA-LTS方法，在BraTS 2020和FeTS 2024上验证其可在保持分割精度的同时提升校准效果，增强临床部署可靠性。
中文摘要 AI 辅助
多模态脑肿瘤分割通常利用多种MRI模态，但由于协议异质性和扫描失败，临床实践中常出现模态采集不完整的情况。尽管近期方法在缺失模态条件下仍能保持分割精度，但它们往往忽视预测可靠性，导致置信度估计校准不当，阻碍了临床应用。现有校准技术大多与模态无关，或假设随着额外模态的加入，预测难度会单调下降。然而在脑肿瘤分割中，预测难度主要取决于缺失的是哪些模态，而非缺失的数量，这会导致特定组合且空间异质的校准误差。为解决该问题，我们提出缺失模态感知局部温度缩放（MMA-LTS），这是一种事后体素级置信度校准方法，它基于模态可用性可学习令牌和体素级难度分数估计空间自适应温度场。在BraTS 2020和FeTS 2024上的实验表明，MMA-LTS在各类缺失模态场景下，既能保持当前最优模型的分割精度，又能改善校准效果，从而提升临床部署的可靠性。
英文摘要
Multimodal brain tumor segmentation typically leverages multiple MRI modalities, yet incomplete modality acquisition is common in clinical practice due to protocol heterogeneity and scan failures. Although recent methods maintain segmentation accuracy under missing modality conditions, they frequently overlook prediction reliability, leading to miscalibrated confidence estimates that hinder clinical adoption. Existing calibration techniques are largely modality-agnostic or assume that prediction difficulty decreases monotonically as additional modalities become available. However, in brain tumor segmentation, prediction difficulty depends primarily on which modalities are absent rather than how many, leading to combination-specific and spatially heterogeneous calibration errors. To address this, we propose Missing Modality-Aware Local Temperature Scaling (MMA-LTS), a post-hoc voxel-wise confidence calibration method. It estimates a spatially adaptive temperature field conditioned on a modality-availability learnable token and a voxel-wise difficulty score. Experiments on BraTS 2020 and FeTS 2024 show that MMA-LTS improves calibration while preserving the segmentation accuracy of state-of-the-art models across diverse missing-modality scenarios, thereby enhancing trustworthiness toward clinical deployment.
发表机构
- Korea Advanced Institute of Science and Technology（韩国科学技术院）
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