Uncertainty-Aware Classification: A Human-Guided Bayesian Deep Learning Framework

doi:10.1007/978-3-032-06593-3_19

2026-01-01

PRE

Kampen, PeterJ. T. *

M. Reimann

Morten Rieger Hannemose

Anders Nymark Christensen

Miriam Kolko

Anders Bjorholm Dahl

Josefine Vilsbøll Sundgaard

DOI：10.1007/978-3-032-06593-3_19

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尽管神经网络在医学图像分析中表现出色，但如何有效结合其预测与人类专业知识仍是临床部署中的关键挑战。我们研究了近似贝叶斯推理中不同随机参数子集的选择对后验预测分布的影响，进而影响人机结合决策模型的性能。通过两个医学分类任务，我们分析了所得模型与人类不确定性的关系。我们发现，不确定性估计与人类不确定性的相关性因随机子集的不同而异。基于这些发现，我们提出了一种框架，通过优化随机子集的选择来改进最终决策模型，该模型考虑了人类不确定性，从而在临床环境中实现更可靠、更可解释的人机预测融合。我们的实现代码公开托管于https://github.com/mkreimann/uncertainty-guided-classification。

Keyword:

Uncertainty

Model calibration

Trustworthy AI

期刊

UNCERTAINTY FOR SAFE UTILIZATION OF MACHINE LEARNING IN MEDICAL IMAGING, UNSURE 2025

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Technical University of Denmark

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university of copenhagen

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