AI RESEARCH
Class Visualizations and Activation Atlases for Enhancing Interpretability in Deep Learning-Based Computational Pathology
arXiv CS.CV
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ArXi:2603.07170v1 Announce Type: new 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 interpretability has not kept pace with model complexity. While attribution- and generative-based methods are common, feature visualization approaches such as class visualizations (CVs) and activation atlases (AAs) have not been systematically evaluated for these models.