Jeroen van der Laak

Radboud University Medical Center

Research Interests

Explore related searches

Contact this professor

Google Scholar

Articles (21)

Collaborators (14)

Claes Lundström

Geert Litjens

Radboud University Medical Center

Milda Pocevičiūtė

Linköping University

Ronald de Krijger

University Medical Center Groningen

Kimmo Kartasalo

Karolinska Institutet

Inti Zlobec

University of Bern

Shadi Albarqouni

Professor of Computational Medical Imaging Research; Group Leader

University of Bonn

Michiel van den Brekel

Academic, Full Professor, null

University of Amsterdam

Konnie Hebeda

department of pathology erasmus university medical center rotterdam the netherlands

Francesco Ciompi

Associate Professor

Radboud University Medical Center

Niccolò Marini

National Institutes of Health

UNITED STATES

Dominique van Midden

Radboud University Medical Center

Bart Smeets

Reseacher/Lecturer

HAN University of Applied Sciences

Suzanne Wetstein

Eindhoven University of Technology

Jeroen van der Laak

Research Interests

Explore related searches

Contact this professor

Articles (21)

Hierarchical Vision Transformers for prostate biopsy grading: Towards bridging the generalization gap

Deep learning-based histopathologic segmentation of peritubular capillaries in kidney transplant biopsies

Pathogenesis of peritoneal high‐grade serous carcinoma after risk‐reducing surgery: a systematic review

Multi-resolution deep learning characterizes tertiary lymphoid structures and their prognostic relevance in solid tumors

LYSTO

Diffusion models for out-of-distribution detection in digital pathology

Automatic data augmentation to improve generalization of deep learning in H&E stained histopathology

The Latent Doctor Model for Modeling Inter-Observer Variability

Pathologists’ first opinions on barriers and facilitators of computational pathology adoption in oncological pathology: an international study

Gigapixel end-to-end training using streaming and attention

Automated Deep Learning-Based Classification of Wilms Tumor Histopathology

Continual learning strategies for cancer-independent detection of lymph node metastases

Semi-Supervised Learning to Automate Tumor Bud Detection in Cytokeratin-Stained Whole-Slide Images of Colorectal Cancer

Predictive uncertainty estimation for out-of-distribution detection in digital pathology

PROACTING: predicting pathological complete response to neoadjuvant chemotherapy in breast cancer from routine diagnostic histopathology biopsies with deep learning

Comparison of consecutive and restained sections for image registration in histopathology.

Evaluation Criteria for Chromosome Instability Detection by FISH to Predict Malignant Progression in Premalignant Glottic Laryngeal Lesions

Convolutional Neural Networks for the Evaluation of Chronic and Inflammatory Lesions in Kidney Transplant Biopsies.

A Decade of <i>GigaScience</i>: The Challenges of Gigapixel Pathology Images

Generalization of Deep Learning in Digital Pathology: Experience in Breast Cancer Metastasis Detection

Unleashing the potential of digital pathology data by training computer-aided diagnosis models without human annotations.

Collaborators (14)

Claes Lundström

Geert Litjens

Milda Pocevičiūtė

Ronald de Krijger

Kimmo Kartasalo

Inti Zlobec

Shadi Albarqouni

Michiel van den Brekel

Konnie Hebeda

Francesco Ciompi

Niccolò Marini

Dominique van Midden

Bart Smeets

Suzanne Wetstein

How do I reach out?

Meet Kite AI