Publications of Jeroen van der Laak

2026

Papers in international journals

  1. M. Rijthoven, W. Aswolinskiy, L. Tessier, R. Salgado, J. van der Laak, F. Ciompi and TIGER consortium, "Analysis of computational tumor-infiltrating lymphocytes in breast cancer from the results of the TIGER challenge", Nature Communications, 2026.
    Abstract DOI
  2. D. Schouten, J. van der Laak, D. Somford, H. Küsters-Vandevelde, N. Khalili and G. Litjens, "Three-dimensional reconstruction of gigapixel whole-mount histopathology specimens with RAPID", Scientific Reports, 2026.
    Abstract DOI
  3. S. Jarkman, M. Lindvall, C. Lundström, D. Treanor and J. van der Laak, "Designing AI Tools for Pathology: A Mixed-Method Study on User Interface Design for Breast Cancer Lymph Node Metastases Detection", Intelligence-Based Medicine, 2026:100396.
    DOI

Preprints

  1. M. Stegeman, L. Philipp, F. van der Graaf, M. D'Amato, C. Grisi, L. Builtjes, J.S. Bosma, J. Lefkes, R. Weber, J. Meakin, T. Koopman, A. Mickan, M. Prokop, E. Smit, G. Litjens, J. van der Laak, B. van Ginneken, M. de Rooij, H. Huisman, C. Jacobs, F. Ciompi and A. Hering, "Designing UNICORN: a Unified Benchmark for Imaging in Computational Pathology, Radiology, and Natural Language", arXiv:2603.02790, 2026.
    Abstract DOI arXiv
  2. C. Grisi, K. Faryna, N. Uysal, V. Agosti, E. Munari, S. Kammerer-Jacquet, P. Salles, Y. Tolkach, R. Büttner, S. Semko, M. Pikul, A. Heidenreich, J. van der Laak and G. Litjens, "Deep Learning From Routine Histology Improves Risk Stratification for Biochemical Recurrence in Prostate Cancer", arXiv:2603.14187, 2026.
    Abstract DOI arXiv

PhD theses

  1. P. Venditelli, "Learning from histopathology images: AI-driven biomarkers for pancreatic ductal adenocarcinoma", PhD thesis, 2026.
    Abstract Url