Explaining Digital Pathology Models via Clustering Activations

• URL: http://arxiv.org/abs/2511.14558v1
• Date: Tue, 18 Nov 2025 15:00:16 GMT
• Title: Explaining Digital Pathology Models via Clustering Activations
• Authors: Adam Bajger, Jan Obdržálek, Vojtěch Kůr, Rudolf Nenutil, Petr Holub, Vít Musil, Tomáš Brázdil,
• Abstract summary: We present a clustering-based explainability technique for digital pathology models based on convolutional neural networks.<n>Our method shows the global behaviour of the model under consideration, while also providing more fine-grained information.<n>We also evaluate the performance of our technique on an existing model for detecting prostate cancer, demonstrating its usefulness.
• Score: 4.206961078715932
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 more fine-grained information. The result clusters can be visualised not only to understand the model, but also to increase confidence in its operation, leading to faster adoption in clinical practice. We also evaluate the performance of our technique on an existing model for detecting prostate cancer, demonstrating its usefulness.

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