arXiv 2606.188602026-06-18cs.CVcs.LG

医学图像分割中对抗模型的不确定性量化

Quantification of Uncertainty with Adversarial Models in Medical Image Segmentation

Institute of Artificial Intelligence, Center for Medical Data Science, Medical University of Vienna, Austria（人工智能研究所、医学数据科学中心、维也纳医学大学，奥地利）；Comprehensive Center for AI in Medicine, Medical University of Vienna, Austria（医学人工智能综合中心、维也纳医学大学，奥地利）；ELLIS Unit Linz, LIT AI Lab and Institute for Machine Learning, Johannes Kepler University Linz, Austria（林茨ELLIS单位、LIT人工智能实验室和机器学习研究所、林茨约瑟夫·冯·克拉夫特大学，奥地利）；Institute for Machine Learning, Johannes Kepler University Linz, Austria（机器学习研究所、林茨约瑟夫·冯·克拉夫特大学，奥地利）；Clinical Research Center for Medical AI, Johannes Kepler University Linz, Austria（医学人工智能临床研究中心、林茨约瑟夫·冯·克拉夫特大学，奥地利）

Hana Jebril, Thomas Pinetz, Günter Klambauer, Hrvoje Bogunović

备注：: Accepted at MICCAI 2026

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AI总结：

提出QUAM-SM后处理框架，通过针对性对抗搜索识别脆弱像素，量化不确定性并分离认知与偶然不确定性，在公开数据集上优于现有方法。

摘要：

可靠的像素级不确定性量化具有通过实现高保真纵向监测和区分真实病理变化与伪影来改变临床工作流程的潜力。理想情况下，这些模型提供关键治疗计划和手术干预所需的稳定性。然而，标准深度学习模型常常遭受校准不良，产生过度自信的预测，掩盖了微妙病理边界处的潜在脆弱性。为了解决这个问题，我们提出了QUAM-SM，一种使用针对性对抗搜索来识别“对抗脆弱”像素的后处理框架。通过主动寻找暴露预测不稳定性的扰动，我们的方法突出了决策最容易被翻转的区域。重要的是，该框架将认知不确定性与偶然不确定性分离。在两个具有多个专家标注的公开数据集上的实验表明，QUAM-SM在可靠性和边界敏感性方面优于标准和最新的不确定性估计方法。代码可在以下网址获取：https://this https URL

英文摘要：

Reliable pixel-level uncertainty quantification holds the potential to transform clinical workflows by enabling high-fidelity longitudinal monitoring and distinguishing true pathological changes from artifacts. Ideally, these models provide the stability required for critical treatment planning and surgical intervention. However, standard deep learning models often suffer from miscalibration, yielding overconfident predictions that mask underlying vulnerabilities at subtle pathological boundaries. To address this, we propose QUAM-SM, a post-hoc framework using targeted adversarial search to identify "adversarially fragile" pixels. By actively seeking perturbations that expose predictive instability, our method highlights regions where decisions are most vulnerable to being flipped. Importantly, the framework disentangles epistemic uncertainty from aleatoric uncertainty. Experiments on two public datasets with multiple expert annotations demonstrate that QUAM-SM outperforms both standard and recent uncertainty estimation approaches in terms of reliability and boundary sensitivity. Code is available at https://github.com/HanaJebril/quam_sm

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。