  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.
