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

Peter J.T. Kampen, Marcel Reimann, Morten Rieger Hannemose, Anders Nymark Christensen, Miriam Kolko, Anders Dahl, Josefine Vilsbøll Sundgaard

Keywords: Uncertainty · Model calibration · Trustworthy AI

Abstract:

While neural networks achieve strong performance in medical
image analysis, effectively combining their predictions with human
expertise remains a critical challenge for clinical deployment. We examine
how different choices of stochastic parameter subsets used in approximate
Bayesian inference impact the posterior predictive distributions
and, consequently, the performance of a combined human-AI decision
model. Using two medical classification tasks, we analyze the relationship
between the resulting model and human uncertainty. We demonstrate
that uncertainty estimates correlate differently with human uncertainty
depending on the stochastic subsets. Building on these findings,
we propose a framework that optimizes the choice of stochastic subsets
to improve a final decision model that considers human uncertainty,
enabling more reliable and interpretable integration of human and AI
predictions in clinical settings. Our implementation is publicly available
at https://github.com/mkreimann/uncertainty-guided-classification

Submission Number: 19