1 citations · 1 across the 7 of their papers we have counts for
6 papers · 1 filter
A Distributional Robustness Margin For Pathology Foundation Models
Clément Grisi, Jeroen van der Laak, Geert Litjens
Pathology foundation models encode non-biological variation introduced by tissue preparation, staining and scanning, enabling shortcut learning that undermines generalisation acros…
Deep Learning From Routine Histology Improves Risk Stratification for Biochemical Recurrence in Prostate Cancer
Clément Grisi, Khrystyna Faryna, Nefise Uysal +11
Accurate prediction of biochemical recurrence (BCR) after radical prostatectomy is critical for guiding adjuvant treatment and surveillance decisions in prostate cancer. However, e…
Designing UNICORN: a Unified Benchmark for Imaging in Computational Pathology, Radiology, and Natural Language
Michelle Stegeman, Lena Philipp, Fennie van der Graaf +20
Foundation models are changing the way we develop medical artificial intelligence. By learning broadly generalizable features across diverse data modalities, a single model can be…
Label-free Concept Based Multiple Instance Learning for Gigapixel Histopathology
Susu Sun, Leslie Tessier, Frédérique Meeuwsen +4
Multiple Instance Learning (MIL) methods allow for gigapixel Whole-Slide Image (WSI) analysis with only slide-level annotations. Interpretability is crucial for safely deploying su…
Masked Attention as a Mechanism for Improving Interpretability of Vision Transformers
Clément Grisi, Geert Litjens, Jeroen van der Laak
Vision Transformers are at the heart of the current surge of interest in foundation models for histopathology. They process images by breaking them into smaller patches following a…
Hierarchical Vision Transformers for Context-Aware Prostate Cancer Grading in Whole Slide Images
Clément Grisi, Geert Litjens, Jeroen van der Laak
Vision Transformers (ViTs) have ushered in a new era in computer vision, showcasing unparalleled performance in many challenging tasks. However, their practical deployment in compu…