3 papers
cs.CV2026
Multi-Teacher Contrastive Distillation for Edge-Efficient Pathology Foundation Models
Tim Lenz, Maurice Heide, Marco Gustav +2
Computational pathology foundation models (PFMs) have advanced whole-slide image analysis. However, their size and inference cost hinder local deployment in pathology departments.…
cs.CV2026
Class Visualizations and Activation Atlases for Enhancing Interpretability in Deep Learning-Based Computational Pathology
Marco Gustav, Fabian Wolf, Christina Glasner +6
The rapid adoption of transformer-based models in computational pathology has enabled prediction of molecular and clinical biomarkers from H&E whole-slide images, yet interpretabil…
cs.CV2026
A deep learning framework for efficient pathology image analysis
Peter Neidlinger, Tim Lenz, Sebastian Foersch +24
Artificial intelligence (AI) has transformed digital pathology by enabling biomarker prediction from high-resolution whole-slide images (WSIs). However, current methods are computa…