Variational Visible Layers: A Practical Framework for Uncertainty Estimation

Abboud, Zeinab

doi:10.1007/978-3-032-05185-1_64

2026-01-01

PRE

Abboud, Zeinab *

Hervé Lombaert

Kadoury, Samuel

DOI：10.1007/978-3-032-05185-1_64

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摘要

En 中文

不确定性估计对于医学成像中的可靠决策至关重要。现有最先进的不确定性方法需要大量的计算开销和复杂的建模。本研究提出并探索了一种简单有效的方法，通过将第一层和/或最后一层（可见层）替换为其变分贝叶斯对应层，将贝叶斯不确定性整合到确定性网络中。这种轻量级修改能够通过平均场变分估计实现不确定性量化，使其适用于实际医学应用。我们在ISIC和LIDC-IDRI数据集上进行分割任务评估，在DermaMNIST和ChestMNIST数据集上进行分类任务评估，分别采用事后和联合训练的可见层。结果表明，变分可见层能够基于不确定性进行失效检测，适用于分布内和近分布外样本，在保持任务性能的同时减少了贝叶斯估计所需的变分参数数量。我们提供了一种易于实现的解决方案，用于将不确定性估计整合到现有工作流中。

Keyword:

Uncertainty

Mean-Field Variational Inference

Bayesian

Neural Networks

Classification

Segmentation

期刊

MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2025, PT XIV

IF:

论文数:

被引数:

机构

universite de montreal

学者数:

4.6W

论文数: 3.8W

被引数: 46

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