UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography

TL;DR

UGPL introduces an uncertainty-guided progressive learning framework for CT image classification, focusing on localized abnormalities by identifying ambiguous regions and refining analysis for improved diagnostic accuracy.

Contribution

It presents a novel global-to-local analysis approach using evidential deep learning and uncertainty guidance, enhancing detection of subtle pathological features in CT images.

Findings

UGPL outperforms state-of-the-art methods on three CT datasets.

Uncertainty-guided component significantly boosts performance.

Progressive learning yields substantial accuracy improvements.

Abstract

Accurate classification of computed tomography (CT) images is essential for diagnosis and treatment planning, but existing methods often struggle with the subtle and spatially diverse nature of pathological features. Current approaches typically process images uniformly, limiting their ability to detect localized abnormalities that require focused analysis. We introduce UGPL, an uncertainty-guided progressive learning framework that performs a global-to-local analysis by first identifying regions of diagnostic ambiguity and then conducting detailed examination of these critical areas. Our approach employs evidential deep learning to quantify predictive uncertainty, guiding the extraction of informative patches through a non-maximum suppression mechanism that maintains spatial diversity. This progressive refinement strategy, combined with an adaptive fusion mechanism, enables UGPL to integrate both contextual information and fine-grained details. Experiments across three CT datasets demonstrate that UGPL consistently outperforms state-of-the-art methods, achieving improvements of 3.29%, 2.46%, and 8.08% in accuracy for kidney abnormality, lung cancer, and COVID-19 detection, respectively. Our analysis shows that the uncertainty-guided component provides substantial benefits, with performance dramatically increasing when the full progressive learning pipeline is implemented. Our code is available at: https://github.com/shravan-18/UGPL