Deep Neural Networks (DNNs) are widely used for visual classification tasks, but their complex computation process and black-box nature hinder decision transparency and interpretability. Class activation maps (CAMs) and recent variants provide ways to visually explain the DNN decision-making process by displaying 'attention' heatmaps of the DNNs. Nevertheless, the CAM explanation only offers relative attention information, that is, on an attention heatmap, we can interpret which image region is more or less important than the others. However, these regions cannot be meaningfully compared across classes, and the contribution of each region to the model's class prediction is not revealed. To address these challenges that ultimately lead to better DNN Interpretation, in this paper, we propose CAPE, a novel reformulation of CAM that provides a unified and probabilistically meaningful assessment of the contributions of image regions. We quantitatively and qualitatively compare CAPE with state-of-the-art CAM methods on CUB and ImageNet benchmark datasets to demonstrate enhanced interpretability. We also test on a cytology imaging dataset depicting a challenging Chronic Myelomonocytic Leukemia (CMML) diagnosis problem. Code is available at: https://github.com/AIML-MED/CAPE.

翻译：深度神经网络（DNN）广泛应用于视觉分类任务，但其复杂的计算过程和黑箱特性阻碍了决策的透明性与可解释性。类激活映射（CAM）及其近期变体通过显示DNN的"注意力"热力图，为可视化解释DNN决策过程提供了途径。然而，CAM解释仅提供相对注意力信息——即在注意力热力图上，我们只能判断图像中哪些区域比其它区域更重要或不重要。但这些区域无法在不同类别间进行有意义的比较，且每个区域对模型类别预测的贡献度也未得到揭示。为解决这些最终影响DNN解释性的挑战，本文提出CAPE——一种对CAM的全新重构方法，可对图像区域的贡献度进行统一且具有概率意义的评估。我们在CUB和ImageNet基准数据集上，将CAPE与最先进的CAM方法进行定量与定性比较，验证其增强后的可解释性。此外，我们还在一个描绘慢性粒单核细胞白血病（CMML）诊断难题的细胞学成像数据集上进行了测试。代码开源地址：https://github.com/AIML-MED/CAPE