3 papers
eess.IV2019
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
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
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…