Modeling multi-scale uncertainty with evidence integration for reliable polyp segmentation.
Journal:
Neural networks : the official journal of the International Neural Network Society
Published Date:
Sep 1, 2025
Abstract
Polyp segmentation is critical in medical image analysis. Traditional methods, while capable of producing precise outputs in well-defined regions, often struggle with blurry or ambiguous areas in medical images, which can lead to errors in clinical decision-making. Additionally, these methods typically generate only a single deterministic segmentation result, failing to account for the inherent uncertainty in the segmentation process. This limitation undermines the reliability of segmentation models in clinical practice, as they lack the ability to provide insights into the confidence or certainty of their predictions, leaving clinicians skeptical of their utility. To address these challenges, we propose a novel multi-scale uncertainty modeling framework for polyp segmentation, grounded in evidence theory. Our approach leverages the Dirichlet distribution to classify pixels within polyp images while integrating uncertainty across different scales. We first employ an Uncertainty Region Enhancement Process (UREP) to refine uncertain regions and Integrated Balance Module (IBM) to dynamically balance the weights between different feature maps for generating semantic fusion feature maps. Subsequently, we utilize two feature extraction sub-networks to learn feature representations from original images and semantic fusion feature maps. We further develop a Multi-scale Evidence Integration Network (MEIN) to robustly model uncertainty through subjective logic, merging results from two sub-networks to ensure a comprehensive understanding of uncertainty and produce reliable segmentation results. In contrast to most existing methods, our approach not only generates segmentation results but also provides uncertainty estimates, offering clinicians valuable insights into the reliability of the predictions. Experimental results on five polyp segmentation datasets demonstrate that our proposed method remains competitive and generates effective uncertainty estimations compared to existing representative methods. The code is available at https://github.com/q1216355254/MEIN.
