Explaining Digital Pathology Models via Clustering Activations

ArXi:2511.14558v2 Announce Type: replace We present a clustering-based explainability technique for digital pathology models based on convolutional neural networks. Unlike commonly used methods based on saliency maps, such as occlusion, GradCAM, or relevance propagation, which highlight regions that contribute the most to the prediction for a single slide, our method shows the global behaviour of the model under consideration, while also providing fine-grained information.