390 citations · 533 across the 5 of their papers we have counts for
5 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…
ECTIL: Label-efficient Computational Tumour Infiltrating Lymphocyte (TIL) assessment in breast cancer: Multicentre validation in 2,340 patients with breast cancer
Yoni Schirris, Rosie Voorthuis, Mark Opdam +18
The level of tumour-infiltrating lymphocytes (TILs) is a prognostic factor for patients with (triple-negative) breast cancer (BC). Computational TIL assessment (CTA) has the potent…
Federated Learning Enables Big Data for Rare Cancer Boundary Detection
Sarthak Pati, Ujjwal Baid, Brandon Edwards +276
Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally shar…
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…