UGPL uses evidential uncertainty maps to select CT image patches for focused re-analysis and reports gains over baselines on kidney, lung, and COVID classification.
Iglovikov, Eugene Khved- chenya, Alex Parinov, Mikhail Druzhinin, and Alexandr A
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
method 1
citation-polarity summary
fields
eess.IV 1years
2025 1verdicts
REJECT 1roles
method 1polarities
use method 1representative citing papers
citing papers explorer
-
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography
UGPL uses evidential uncertainty maps to select CT image patches for focused re-analysis and reports gains over baselines on kidney, lung, and COVID classification.