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. Our code is available at:
https://github.com/shravan-18/UGPL
687f68bde40f811571a1b6b8
Shravan Venkatraman
claimed_verified
2025-07-22T13:05:16.985Z
6555bae917132f996e84471e
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/uYSDeQJgrWAeIoq1puR55.jpeg
Shravan Venkatraman
user
shravvvv
687f68bde40f811571a1b6b9
Pavan Kumar S
admin_assigned
2025-07-22T14:27:04.697Z
67cd28a3b4ce0337655f790b
/avatars/fab48361a71519585bf641bccc81d096.svg
pavankumars
user
PavanKumarS
687f68bde40f811571a1b6ba
Rakesh Raj Madavan
687f68bde40f811571a1b6bb
Chandrakala S
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. Our code is available at:
https://github.com/shravan-18/UGPL
UGPL, an uncertainty-guided progressive learning framework, enhances CT image classification by focusing on ambiguous regions and integrating contextual and detailed information, outperforming existing methods across various datasets.
evidential deep learning uncertainty-guided progressive learning global-to-local analysis non-maximum suppression adaptive fusion mechanism kidney abnormality detection lung cancer detection COVID-19 detection