721 citations · 1.6k across the 16 of their papers we have counts for
17 papers
A Distributional Robustness Margin For Pathology Foundation Models
Clément Grisi, Jeroen van der Laak, Geert Litjens
Pathology foundation models encode non-biological variation introduced by tissue preparation, staining and scanning, enabling shortcut learning that undermines generalisation acros…
Improving Quality Control of Whole Slide Images by Explicit Artifact Augmentation
Artur Jurgas, Marek Wodzinski, Marina D'Amato +3
The problem of artifacts in whole slide image acquisition, prevalent in both clinical workflows and research-oriented settings, necessitates human intervention and re-scanning. Ove…
Masked Attention as a Mechanism for Improving Interpretability of Vision Transformers
Clément Grisi, Geert Litjens, Jeroen van der Laak
Vision Transformers are at the heart of the current surge of interest in foundation models for histopathology. They process images by breaking them into smaller patches following a…
Uncertainty-guided annotation enhances segmentation with the human-in-the-loop
Nadieh Khalili, Joey Spronck, Francesco Ciompi +2
Deep learning algorithms, often critiqued for their 'black box' nature, traditionally fall short in providing the necessary transparency for trusted clinical use. This challenge is…
Hierarchical Vision Transformers for Context-Aware Prostate Cancer Grading in Whole Slide Images
Clément Grisi, Geert Litjens, Jeroen van der Laak
Vision Transformers (ViTs) have ushered in a new era in computer vision, showcasing unparalleled performance in many challenging tasks. However, their practical deployment in compu…
Domain adaptation strategies for cancer-independent detection of lymph node metastases
Péter Bándi, Maschenka Balkenhol, Marcory van Dijk +3
Recently, large, high-quality public datasets have led to the development of convolutional neural networks that can detect lymph node metastases of breast cancer at the level of ex…
Automated risk classification of colon biopsies based on semantic segmentation of histopathology images
John-Melle Bokhorst, Iris D. Nagtegaal, Filippo Fraggetta +5
Artificial Intelligence (AI) can potentially support histopathologists in the diagnosis of a broad spectrum of cancer types. In colorectal cancer (CRC), AI can alleviate the labori…
Comparison of Consecutive and Re-stained Sections for Image Registration in Histopathology
Johannes Lotz, Nick Weiss, Jeroen van der Laak +1
Purpose: In digital histopathology, virtual multi-staining is important for diagnosis and biomarker research. Additionally, it provides accurate ground-truth for various deep-learn…
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…
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…
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…
Neural Image Compression for Gigapixel Histopathology Image Analysis
David Tellez, Geert Litjens, Jeroen van der Laak +1
We propose Neural Image Compression (NIC), a two-step method to build convolutional neural networks for gigapixel image analysis solely using weak image-level labels. First, gigapi…
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…
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…
Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images
Babak Ehteshami Bejnordi, Guido Zuidhof, Maschenka Balkenhol +6
Automated classification of histopathological whole-slide images (WSI) of breast tissue requires analysis at very high resolutions with a large contextual area. In this paper, we p…
Comparison of Different Methods for Tissue Segmentation in Histopathological Whole-Slide Images
Péter Bándi, Rob van de Loo, Milad Intezar +5
Tissue segmentation is an important pre-requisite for efficient and accurate diagnostics in digital pathology. However, it is well known that whole-slide scanners can fail in detec…
The importance of stain normalization in colorectal tissue classification with convolutional networks
Francesco Ciompi, Oscar Geessink, Babak Ehteshami Bejnordi +6
The development of reliable imaging biomarkers for the analysis of colorectal cancer (CRC) in hematoxylin and eosin (H&E) stained histopathology images requires an accurate and rep…