Class visualizations and activation atlases for computational pathology.

Marco Gustav¹, Fabian Wolf¹, Christina Glasner², Nic G Reitsam³, Stefan Schulz², Kira Aschenbroich², Bruno Märkl⁴, Sebastian Foersch², Jakob Nikolas Kather⁵

¹Else Kroener Fresenius Center for Digital Health, Faculty of Medicine, TUD Dresden University of Technology, Dresden, Germany.

²Institute of Pathology, University Medical Center Mainz, Mainz, Germany.

³Else Kroener Fresenius Center for Digital Health, Faculty of Medicine, TUD Dresden University of Technology, Dresden, Germany; Pathology, Faculty of Medicine, University of Augsburg, Augsburg, Germany; Bavarian Cancer Research Center (BZKF), Augsburg, Germany.

⁴Pathology, Faculty of Medicine, University of Augsburg, Augsburg, Germany; Bavarian Cancer Research Center (BZKF), Augsburg, Germany.

⁵Else Kroener Fresenius Center for Digital Health, Faculty of Medicine, TUD Dresden University of Technology, Dresden, Germany; Department of Medicine I, Faculty of Medicine, TUD Dresden University of Technology, Dresden, Germany; Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany; Pathology & Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK. Electronic address: kather.jn@tu-dresden.de.

PubMed DOI

Abstract

The clinical promise of computational pathology increasingly depends on foundation model-based pipelines, yet the morphological concepts encoded by these systems remain poorly understood. We address this gap with a concept-level visualization framework using class visualizations (CVs) and activation atlases (AAs) in a pathology foundation-model setting across colorectal tissue and multi-organ canc…

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