activity
20172026
most citedContext-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images

7 citations · 8 across the 5 of their papers we have counts for

collaborators

7 papers

cs.CV2026

DALPHIN: Benchmarking Digital Pathology AI Copilots Against Pathologists on an Open Multicentric Dataset

Carlijn Lems, Sander Moonemans, Natálie Klubíčková +53

Foundation models with visual question answering capabilities for digital pathology are emerging. Such unprecedented technology requires independent benchmarking to assess its pote…

q-bio.QM2025

A Multicentric Dataset for Training and Benchmarking Breast Cancer Segmentation in H&E Slides

Carlijn Lems, Leslie Tessier, John-Melle Bokhorst +18

Automated semantic segmentation of whole-slide images (WSIs) stained with hematoxylin and eosin (H&E) is essential for large-scale artificial intelligence-based biomarker analysis…

eess.IV20201 cited

Automated Scoring of Nuclear Pleomorphism Spectrum with Pathologist-level Performance in Breast Cancer

Caner Mercan, Maschenka Balkenhol, Roberto Salgado +13

Nuclear pleomorphism, defined herein as the extent of abnormalities in the overall appearance of tumor nuclei, is one of the components of the three-tiered breast cancer grading. G…

eess.IV2020

HookNet: multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide images

Mart van Rijthoven, Maschenka Balkenhol, Karina Siliņa +2

We propose HookNet, a semantic segmentation model for histopathology whole-slide images, which combines context and details via multiple branches of encoder-decoder convolutional n…

eess.IV2020

Artificial Intelligence Assistance Significantly Improves Gleason Grading of Prostate Biopsies by Pathologists

Wouter Bulten, Maschenka Balkenhol, Jean-Joël Awoumou Belinga +17

While the Gleason score is the most important prognostic marker for prostate cancer patients, it suffers from significant observer variability. Artificial Intelligence (AI) systems…

cs.CV2018

Whole-Slide Mitosis Detection in H&E Breast Histology Using PHH3 as a Reference to Train Distilled Stain-Invariant Convolutional Networks

David Tellez, Maschenka Balkenhol, Irene Otte-Holler +10

Manual counting of mitotic tumor cells in tissue sections constitutes one of the strongest prognostic markers for breast cancer. This procedure, however, is time-consuming and erro…