通过不确定性引导逐步分析，提升CT图像病灶分类精度。
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography
- 先识别诊断模糊区域，再聚焦分析关键部位。
- 在三组CT数据上准确率提升最高达8.08%。
- 适合需要精准定位病灶的医学影像分析场景。
CT图像准确分类对诊断与治疗规划至关重要，但现有方法常因病灶特征细微且空间分布多样而表现不佳。当前方法通常均匀处理图像，难以捕捉需专注分析的局部异常。本文提出UGPL，一种基于不确定性的渐进式学习框架，通过全局到局部分析：先识别诊断模糊区域，再对这些关键区域进行细致检查。该方法采用证据深度学习量化预测不确定性，利用非最大值抑制机制提取具有空间多样性的信息块。结合自适应融合机制，UGPL可同时整合上下文信息与细粒度细节。在三个CT数据集上的实验表明，UGPL持续优于现有最优方法，分别在肾异常、肺癌和新冠检测任务中实现3.29%、2.46%和8.08%的准确率提升。分析显示，不确定性引导组件带来显著收益，完整渐进学习流程使性能大幅提升。代码已开源：https://github.com/shravan-18/UGPL。
原文摘要 · Abstract (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
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