activity
20182020
most citedAutomated Gleason Grading of Prostate Biopsies using Deep Learning

721 citations · 1.7k across the 6 of their papers we have counts for

collaborators

7 papers

eess.IV2020

Detection of prostate cancer in whole-slide images through end-to-end training with image-level labels

Hans Pinckaers, Wouter Bulten, Jeroen van der Laak +1

Prostate cancer is the most prevalent cancer among men in Western countries, with 1.1 million new diagnoses every year. The gold standard for the diagnosis of prostate cancer is a…

eess.IV2020★ 173 cited

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…

eess.IV2019★ 721 cited

Automated Gleason Grading of Prostate Biopsies using Deep Learning

Wouter Bulten, Hans Pinckaers, Hester van Boven +6

The Gleason score is the most important prognostic marker for prostate cancer patients but suffers from significant inter-observer variability. We developed a fully automated deep…

cs.CV2019★ 5 cited

Dealing with Label Scarcity in Computational Pathology: A Use Case in Prostate Cancer Classification

Koen Dercksen, Wouter Bulten, Geert Litjens

Large amounts of unlabelled data are commonplace for many applications in computational pathology, whereas labelled data is often expensive, both in time and cost, to acquire. We i…

cs.CV2019★ 632 cited

Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology

David Tellez, Geert Litjens, Peter Bandi +4

Stain variation is a phenomenon observed when distinct pathology laboratories stain tissue slides that exhibit similar but not identical color appearance. Due to this color shift b…

cs.CV2018★ 154 cited

Epithelium segmentation using deep learning in H&E-stained prostate specimens with immunohistochemistry as reference standard

Wouter Bulten, Péter Bándi, Jeffrey Hoven +7

Prostate cancer (PCa) is graded by pathologists by examining the architectural pattern of cancerous epithelial tissue on hematoxylin and eosin (H&E) stained slides. Given the impor…