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

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.

We introduce Uncertainty-Guided Progressive Learning (UGPL), a novel framework that mimics diagnostic behavior by performing global analysis followed by focused examination of uncertain regions. UGPL addresses limitations of uniform processing by dynamically allocating computational resources where needed. Our framework first employs a global uncertainty estimator to perform initial classification and generate pixel-wise uncertainty maps, then selects high-uncertainty regions for detailed analysis through a local refinement network. These multi-resolution analyses are combined via an adaptive fusion module that weights predictions based on confidence. Unlike existing methods that treat uncertainty merely as an output signal, UGPL explicitly uses it to guide computational focus, maintaining efficiency while improving performance on diagnostically challenging regions. UGPL processes the input CT image to produce both classification probabilities and an uncertainty map that guides the extraction of high-uncertainty patches using non-maximum suppression. Each patch undergoes high-resolution analysis through a local refinement network, producing patch-specific classification scores and confidence estimates. The adaptive fusion module then integrates global and local predictions using learned weights based on their estimated reliability. Multiple specialized loss functions are jointly optimized, guiding components to work in tandem, adapt according to diagnostic difficulty, and improve performance over uniform processing.