Publications (9)
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…
Beyond Attention Heatmaps: How to Get Better Explanations for Multiple Instance Learning Models in Histopathology
Mina Jamshidi Idaji, Julius Hense, Tom Neuhäuser +12
Multiple instance learning (MIL) has enabled substantial progress in computational histopathology, where a large amount of patches from gigapixel whole slide images are aggregated…
Do Histopathological Foundation Models Eliminate Batch Effects? A Comparative Study
Jonah Kömen, Hannah Marienwald, Jonas Dippel +1
Deep learning has led to remarkable advancements in computational histopathology, e.g., in diagnostics, biomarker prediction, and outcome prognosis. Yet, the lack of annotated data…
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…
xMIL: Insightful Explanations for Multiple Instance Learning in Histopathology
Julius Hense, Mina Jamshidi Idaji, Oliver Eberle +7
Multiple instance learning (MIL) is an effective and widely used approach for weakly supervised machine learning. In histopathology, MIL models have achieved remarkable success in…
Mind the Gap: Continuous Magnification Sampling for Pathology Foundation Models
Alexander Möllers, Julius Hense, Florian Schulz +3
In histopathology, pathologists examine both tissue architecture at low magnification and fine-grained morphology at high magnification. Yet, the performance of pathology foundatio…