143 citations · 147 across the 4 of their papers we have counts for
4 papers
From Explainable to Explained AI: Ideas for Falsifying and Quantifying Explanations
Yoni Schirris, Eric Marcus, Jonas Teuwen +2
Explaining deep learning models is essential for clinical integration of medical image analysis systems. A good explanation highlights if a model depends on spurious features that…
Foundation Models in Medical Imaging: A Review and Outlook
Vivien van Veldhuizen, Vanessa Botha, Chunyao Lu +10
Foundation models (FMs) are changing the way medical images are analyzed by learning from large collections of unlabeled data. Instead of relying on manually annotated examples, FM…
WeakSTIL: Weak whole-slide image level stromal tumor infiltrating lymphocyte scores are all you need
Yoni Schirris, Mendel Engelaer, Andreas Panteli +3
We present WeakSTIL, an interpretable two-stage weak label deep learning pipeline for scoring the percentage of stromal tumor infiltrating lymphocytes (sTIL%) in H&E-stained whole-…
DeepSMILE: Contrastive self-supervised pre-training benefits MSI and HRD classification directly from H&E whole-slide images in colorectal and breast cancer
Yoni Schirris, Efstratios Gavves, Iris Nederlof +2
We propose a Deep learning-based weak label learning method for analyzing whole slide images (WSIs) of Hematoxylin and Eosin (H&E) stained tumor tissue not requiring pixel-level or…