UTMap: Triplet Neural Network for Uncertainty Medical Image Analysis

• Rafael Souza e Silva USP
• André C. P. L. F. de Carvalho USP

DOI: https://doi.org/10.5753/eniac.2025.14372

Abstract

This paper proposes a framework to enhance the reliability of deep classifiers in medical imaging by combining visualization and uncertainty quantification through meta-learning. The methodology employs a Triplet Neural Network (UTMap) to project instances into the Instance Uncertainty Space (IUS), which highlights patterns of confidence and uncertainty. Additionally, the Neighborhood Reliability Score (NRS) metric is introduced to estimate uncertainty based on the spatial relationships within the IUS. Experimental results show that the IUS effectively represents classifier behavior and that the NRS achieves competitive performance compared to traditional uncertainty estimation algorithms in distinguishing between correct and incorrect predictions.

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