15.4k citations · 20.7k across the 40 of their papers we have counts for
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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…
Deep Learning From Routine Histology Improves Risk Stratification for Biochemical Recurrence in Prostate Cancer
Clément Grisi, Khrystyna Faryna, Nefise Uysal +11
Accurate prediction of biochemical recurrence (BCR) after radical prostatectomy is critical for guiding adjuvant treatment and surveillance decisions in prostate cancer. However, e…
Designing UNICORN: a Unified Benchmark for Imaging in Computational Pathology, Radiology, and Natural Language
Michelle Stegeman, Lena Philipp, Fennie van der Graaf +20
Foundation models are changing the way we develop medical artificial intelligence. By learning broadly generalizable features across diverse data modalities, a single model can be…
Democratising Pathology Co-Pilots: An Open Pipeline and Dataset for Whole-Slide Vision-Language Modelling
Sander Moonemans, Sebastiaan Ram, Frédérique Meeuwsen +4
Vision-language models (VLMs) have the potential to become co-pilots for pathologists. However, most VLMs either focus on small regions of interest within whole-slide images, provi…
Self-supervised large-scale kidney abnormality detection in drug safety assessment studies
Ivan Slootweg, Natalia P. García-De-La-Puente, Geert Litjens +1
Kidney abnormality detection is required for all preclinical drug development. It involves a time-consuming and costly examination of hundreds to thousands of whole-slide images pe…
Prototype-Based Multiple Instance Learning for Gigapixel Whole Slide Image Classification
Susu Sun, Dominique van Midden, Geert Litjens +1
Multiple Instance Learning (MIL) methods have succeeded remarkably in histopathology whole slide image (WSI) analysis. However, most MIL models only offer attention-based explanati…
Label-free Concept Based Multiple Instance Learning for Gigapixel Histopathology
Susu Sun, Leslie Tessier, Frédérique Meeuwsen +4
Multiple Instance Learning (MIL) methods allow for gigapixel Whole-Slide Image (WSI) analysis with only slide-level annotations. Interpretability is crucial for safely deploying su…
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…
Medical diffusion on a budget: Textual Inversion for medical image generation
Bram de Wilde, Anindo Saha, Maarten de Rooij +2
Diffusion models for text-to-image generation, known for their efficiency, accessibility, and quality, have gained popularity. While inference with these systems on consumer-grade…
Understanding metric-related pitfalls in image analysis validation
Annika Reinke, Minu D. Tizabi, Michael Baumgartner +75
Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation int…
Metrics reloaded: Recommendations for image analysis validation
Lena Maier-Hein, Annika Reinke, Patrick Godau +71
Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen…
Streaming convolutional neural networks for end-to-end learning with multi-megapixel images
Hans Pinckaers, Bram van Ginneken, Geert Litjens
Due to memory constraints on current hardware, most convolution neural networks (CNN) are trained on sub-megapixel images. For example, most popular datasets in computer vision con…
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…
A large annotated medical image dataset for the development and evaluation of segmentation algorithms
Amber L. Simpson, Michela Antonelli, Spyridon Bakas +21
Semantic segmentation of medical images aims to associate a pixel with a label in a medical image without human initialization. The success of semantic segmentation algorithms is c…
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…
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…
Unsupervised Prostate Cancer Detection on H&E using Convolutional Adversarial Autoencoders
Wouter Bulten, Geert Litjens
We propose an unsupervised method using self-clustering convolutional adversarial autoencoders to classify prostate tissue as tumor or non-tumor without any labeled training data.…
Training convolutional neural networks with megapixel images
Hans Pinckaers, Geert Litjens
To train deep convolutional neural networks, the input data and the intermediate activations need to be kept in memory to calculate the gradient descent step. Given the limited mem…
Computer-aided diagnosis of lung carcinoma using deep learning - a pilot study
Zhang Li, Zheyu Hu, Jiaolong Xu +10
Aim: Early detection and correct diagnosis of lung cancer are the most important steps in improving patient outcome. This study aims to assess which deep learning models perform be…
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…