UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based
  Classification in Computed Tomography
Authors: name: Shravan Venkatraman, name: Pavan Kumar S, name: Rakesh Raj Madavan, name: Chandrakala S

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