J.S. Bosma, K. Dercksen, L. Builtjes, R. André, C. Roest, S. Fransen, C. Noordman, M. Navarro-Padilla, J. Lefkes, N. Alves, M. de Grauw, L. van Eekelen, J. Spronck, M. Schuurmans, B. de Wilde, W. Hendrix, W. Aswolinskiy, A. Saha, J. Twilt, D. Geijs, J. Veltman, D. Yakar, M. de Rooij, F. Ciompi, A. Hering, J. Geerdink, H. Huisman, O. behalf of the consortium, M. de Grauw, L. van Eekelen, B. de Wilde, Q. van Lohuizen, M. Stegeman, K. Rutten, I. Smit, G. Stultiens, C. Overduin, M. Rutten, E. Scholten, R. van der Post, K. Grünberg, S. Vos, E. Taken, I. Nagtegaal, A. Mickan, M. Groeneveld, P. Gerke, J. Meakin, M. Looijen-Salamon, T. de Haas, F. Hoitsma, M. D'Amato and M. de Rooij, "The DRAGON benchmark for clinical NLP",
npj Digital Medicine,
2025;8.
N. Khalili, J. Spronck, F. Ciompi, J. der Van Laak and G. Litjens, "A human-in-the-loop framework for refining deep learning models in pathology segmentation",
Medical Imaging 2025: Digital and Computational Pathology,
2025:21.
V. Eekelen, Leander, J. Spronck, M. Looijen-Salamon, S. Vos, E. Munari, I. Girolami, A. Eccher, B. Acs, C. Boyaci, G. de Souza, M. Demirel-Andishmand, L. Meesters, D. Zegers, L. van der Woude, W. Theelen, M. van den Heuvel, K. Grünberg, B. van Ginneken, J. van der Laak and F. Ciompi, "Comparing deep learning and pathologist quantification of cell-level PD-L1 expression in non-small cell lung cancer whole-slide images",
Scientific Reports,
2024;14.
N. Khalili, J. Spronck, F. Ciompi, J. van der Laak and G. Litjens, "Uncertainty-guided annotation enhances segmentation with the human-in-the-loop",
arXiv:2404.07208,
2024.
L. Eekelen, G. den Heuvel, L. Studer, J. Spronck, K. Grünberg, D. Zegers, J. der Laak, M. den Heuvel and F. Ciompi, "Immunotherapy response prediction for non-small cell lung cancer is improved by using cell-graphs of the tumor microenvironment",
European Congress on Digital Pathology,
2024.
J. Spronck, T. Gelton, L. van Eekelen, J. Bogaerts, L. Tessier, M. van Rijthoven, L. van der Woude, M. van den Heuvel, W. Theelen, J. van der Laak and F. Ciompi, "nnUNet meets pathology: bridging the gap for application to whole-slide images and computational biomarkers",
Medical Imaging with Deep Learning,
2023.
J. Spronck, L. Eekelen, L. Tessier, J. Bogaerts, L. van der Woude, M. van den Heuvel, W. Theelen and F. Ciompi, "Deep learning-based quantification of immune infiltrate for predicting response to pembrolizumab from pre-treatment biopsies of metastatic non-small cell lung cancer: A study on the PEMBRO-RT phase II trial",
Immuno-Oncology and Technology,
2022.