3 citations · 6 across the 8 of their papers we have counts for
9 papers
Sparse concept attribution for histomorphological hypothesis generation from whole-slide classifiers
Tristan Lazard, Kenza Bouzid, Julius Hense +6
Histology images contain rich morphological information and can provide insights into pathological processes. However, deriving hypotheses relating morphological phenotypes to clin…
Symb-xMIL: Symbolic Explanations for Multiple Instance Learning in Digital Pathology
Yanqing Luo, Julius Hense, Niklas Prenißl +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…
In-Context Multiple Instance Learning
Alexander Möllers, Marvin Sextro, Julius Hense +2
Multiple Instance Learning (MIL) addresses problems where supervision is available at the level of bags of instances and has been successfully applied in fields ranging from comput…
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…
Towards Robust Foundation Models for Digital Pathology
Jonah Kömen, Edwin D. de Jong, Julius Hense +9
Biomedical Foundation Models (FMs) are rapidly transforming AI-enabled healthcare research and entering clinical validation. However, their susceptibility to learning non-biologica…
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…
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…
AI-based Anomaly Detection for Clinical-Grade Histopathological Diagnostics
Jonas Dippel, Niklas Prenißl, 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…
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…