UGPL：基于不确定性的渐进式学习用于计算机断层扫描中的证据分类

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

July 18, 2025

作者: Shravan Venkatraman, Pavan Kumar S, Rakesh Raj Madavan, Chandrakala S

cs.AI

摘要

计算机断层扫描（CT）图像的精确分类对于诊断和治疗规划至关重要，然而现有方法在处理病理特征的微妙性和空间多样性时往往面临挑战。当前方法通常对图像进行统一处理，限制了其检测需要重点分析的局部异常的能力。我们提出了UGPL，一种不确定性引导的渐进学习框架，该框架通过首先识别诊断模糊区域，然后对这些关键区域进行详细检查，实现从全局到局部的分析。我们的方法采用证据深度学习来量化预测不确定性，通过非极大值抑制机制引导信息丰富区域的提取，同时保持空间多样性。这种渐进细化策略与自适应融合机制相结合，使UGPL能够整合上下文信息和细粒度细节。在三个CT数据集上的实验表明，UGPL在肾脏异常、肺癌和COVID-19检测的准确率上分别提高了3.29%、2.46%和8.08%，持续超越现有最先进方法。我们的分析显示，不确定性引导组件带来了显著优势，当完整实施渐进学习流程时，性能显著提升。我们的代码可在以下网址获取：https://github.com/shravan-18/UGPL。

English

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