3 papers
cs.CV2026
Symb-xMIL: Symbolic Explanations for Multiple Instance Learning in Digital Pathology
Yanqing Luo, Julius Hense, Niklas PreniÃl +4
Explanations of multiple instance learning (MIL) models are widely used for validation and discovery in digital histopathology. Existing methods primarily rely on heatmaps that hig…
eess.IV2025
MeDi: Metadata-Guided Diffusion Models for Mitigating Biases in Tumor Classification
David Jacob Drexlin, Jonas Dippel, Julius Hense +4
Deep learning models have made significant advances in histological prediction tasks in recent years. However, for adaptation in clinical practice, their lack of robustness to vary…
cs.AI2024
AI-based Anomaly Detection for Clinical-Grade Histopathological Diagnostics
Jonas Dippel, Niklas PreniÃl, Julius Hense +10
While previous studies have demonstrated the potential of AI to diagnose diseases in imaging data, clinical implementation is still lagging behind. This is partly because AI models…