Publications of Geert Litjens

all all years 2026 2025 2024 2023 2022 2021 2020 2019 2018 2017 2016 2015 2014 2013 2012 and before

2026

Papers in international journals

  1. D. Peeters, B. Obreja, N. Antonissen, Z. Saghir, U. Pastorino, M. Silva, G. de Bock, H. Gietema, F. Gleeson, M. Heuvelmans, S. Lam, G. Litjens, F. Mohamed Hoesein, C. Schaefer-Prokop, E. Scholten, A. Snoeckx, E. van der Heijden, R. Vliegenthart, M. Prokop, C. Jacobs and O. behalf of the Consortium, "Benchmarking of AI and Radiologists for Indeterminate Lung Nodule Malignancy Risk Estimation on Screening CT: The LUNA25 Challenge", Radiology: Artificial Intelligence, 2026.
    Abstract DOI PMID
  2. K. Villiamsson, L. Fornstedt, G. Litjens, A. Amir, N. Sjöblom, A. Kaatonen, O. Vesala, F. Yacob, J. Paoli and N. Neittaanmäki, "Detection of basal cell carcinoma on whole-slide images from Mohs micrographic surgery using weakly supervised learning", JAAD International, 2026;28:76-85.
    DOI
  3. R. Spaans, N. Khalili, G. Litjens and J. van Basten, "Tussen potentie en praktijk: AI als motor voor zorgtransformatie bij prostaatkanker", Tijdschrift voor Urologie, 2026.
    Abstract DOI
  4. N. Uysal, C. Grisi, K. Faryna, J. van Ipenburg and G. Litjens, "Towards patient-level Gleason grading from AI-based analysis of individual sections", Scientific Reports, 2026.
    Abstract DOI
  5. E. Munari, P. Antonini, L. Cima, R. Polati, A. Caliò, S. Gobbo, M. Colecchia, G. Netto, A. Antonelli, R. Bertolo, C. Grisi, G. Litjens and M. Brunelli, "The evolution of prostate cancer grading: from Gleason score to risk taxonomy and the artificial intelligence revolution", Virchows Archiv, 2026.
    Abstract DOI
  6. E. Munari, R. Polati, P. Antonini, D. Segala, L. Cima, A. Porcaro, A. Antonelli, R. Bertolo, G. Bogina, F. Tavora, A. Acosta, A. Caliò, K. Faryna, G. Litjens, G. Martignoni and M. Brunelli, "Atypical intraductal proliferation (AIP) of the prostate: a borderline lesion with important clinical implications", Precision Pathology, 2026;1:100004.
    DOI
  7. J. van der Zande, L. Alvarez-Florez, R. Volleberg, C. Brás, D. Karkalousos, R. Nijveldt, N. van Royen, T. Leiner, N. Khalili, G. Litjens, J. Thannhauser and I. Isgum, "Deep Learning for Cardiac Image Analysis", JACC: Cardiovascular Imaging, 2026.
    DOI
  8. F. Khoraminia, M. Olislagers, F. de Jong, F. Akram, A. Nakauma Gonzalez, D. Lichtenberg, A. Stubbs, J. Costello, L. Rijstenberg, G. van Leenders, A. Vrieling, K. Aben, L. Kiemeney, R. Hoedemaeker, C. Bangma, S. Vermeulen, G. Litjens, N. Khalili and T. Zuiverloon, "Predicting bladder cancer molecular subtypes linked to bacillus Calmette-Guerin response from histology images using deep learning", Preprint, 2026.
    Abstract DOI
  9. 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

Preprints

  1. S. Moonemans, S. Ram, F. Meeuwsen, C. Lems, J. van der Laak, G. Litjens and F. Ciompi, "Democratising Pathology Co-Pilots: An Open Pipeline and Dataset for Whole-Slide Vision-Language Modelling", arXiv:2512.17326, 2026.
    arXiv
  2. C. Grisi, J. van der Laak and G. Litjens, "A Distributional Robustness Margin For Pathology Foundation Models", arXiv:2607.25497, 2026.
    Abstract DOI arXiv
  3. S. Innani, S. You, A. Shephard, B. Baheti, F. Ciompi, J. Yeong, N. Rajpoot, M. Feldman, S. Kammerer-Jacquet, D. Makris, G. Litjens, A. Martel, J. Lipkova, A. Khademi, S. Bakas and F. SIG-CompPath, "Explainable Artificial Intelligence (XAI) in Computational Pathology: Definitions, Taxonomy, and Recommendations", arXiv:2608.28820, 2026.
    Abstract DOI arXiv
  4. Q. Da, Y. Chen, M. Ju, Z. Ji, A. Zhou, W. Wang, M. Abikenari, P. Chikontwe, G. Larghero, B. Chen, P. Neidlinger, D. Zhong, S. Wang, W. Xu, D. Williamson, G. Corredor, S. Yang, L. Lu, X. Han, K. Yu, J. Huang, L. Barisoni, G. Litjens, A. Madabhushi, L. Zhu, C. Wang, J. Zhao and W. Hu, "Computational Pathology in the Era of Emerging Foundation and Agentic AI -- International Expert Perspectives on Clinical Integration and Translational Readiness", arXiv:2603.05884, 2026.
    Abstract DOI arXiv
  5. 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
  6. R. Spaans, C. Chia, T. Wang, A. Kowalewski, P. Khachatryan, D. Oliveira, K. Faryna, J. van Basten, G. Litjens and N. Khalili, "CHIMERA Challenge: Biochemical Recurrence Prediction in Prostate Cancer Patients using multimodal datasets", arXiv:2608.21497, 2026.
    Abstract DOI arXiv
  7. 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

PhD theses

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