1 citations · 1 across the 2 of their papers we have counts for
2 papers
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…
eess.IV2024★ 1 cited
Assessing the Performance of Deep Learning for Automated Gleason Grading in Prostate Cancer
Dominik Müller, Philip Meyer, Lukas Rentschler +10
Prostate cancer is a dominant health concern calling for advanced diagnostic tools. Utilizing digital pathology and artificial intelligence, this study explores the potential of 11…