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

arXiv 2507.14102 ↗First posted 18 Jul 2025eess.IV cs.CV cs.LGcited by 0

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Abstract (arXiv)

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

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32 of 75 references.

References · 32

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  3. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  4. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
  5. 2016Densely Connected Convolutional Networkscited by 6,720
  6. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,442
  7. 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 5,045
  8. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,795
  9. 2020Training data-efficient image transformers & distillation through attentioncited by 4,586
  10. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,448
  11. 2018ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Designcited by 1,136
  12. 2021CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classificationcited by 806
20 more
  1. 2018Evidential Deep Learning to Quantify Classification Uncertaintycited by 732
  2. 2018Albumentations: fast and flexible image augmentationscited by 560
  3. 2021Co-Scale Conv-Attentional Image Transformerscited by 224
  4. 2021Recent advances and clinical applications of deep learning in medical image analysiscited by 104
  5. 2020COVID-CT-Dataset: A CT Scan Dataset about COVID-19cited by 94
  6. 2019Supervised Uncertainty Quantification for Segmentation with Multiple Annotationscited by 42
  7. 2021Trustworthy Multimodal Regression with Mixture of Normal-inverse Gamma Distributionscited by 39
  8. 2020Quantifying and Leveraging Predictive Uncertainty for Medical Image Assessmentcited by 26
  9. 2020An Uncertainty-aware Transfer Learning-based Framework for Covid-19 Diagnosiscited by 22
  10. 2022Region-Based Evidential Deep Learning to Quantify Uncertainty and Improve Robustness of Brain Tumor Segmentationcited by 21
  11. 2022A survey on attention mechanisms for medical applications: are we moving towards better algorithms?cited by 15
  12. 2022Lymphoma segmentation from 3D PET-CT images using a deep evidential networkcited by 12
  13. 2022Uncertainty Quantification for Deep Unrolling-Based Computational Imagingcited by 10
  14. 2021RCoNet: Deformable Mutual Information Maximization and High-order Uncertainty-aware Learning for Robust COVID-19 Detectioncited by 6
  15. 2023EVIL: Evidential Inference Learning for Trustworthy Semi-supervised Medical Image Segmentationcited by 6
  16. 2024An Evidential-enhanced Tri-Branch Consistency Learning Method for Semi-supervised Medical Image Segmentationcited by 5
  17. 20233D Arterial Segmentation via Single 2D Projections and Depth Supervision in Contrast-Enhanced CT Imagescited by 4
  18. 2021Uncertainty Quantification in Medical Image Segmentation with Multi-decoder U-Netcited by 2
  19. 2024Deep Evidential Learning for Radiotherapy Dose Predictioncited by 2
  20. 2024Weakly Supervised Learning of Cortical Surface Reconstruction from Segmentationscited by 2

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