Publications of Joey Spronck
Papers in international journals
- 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.
Preprints
- 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.
Papers in conference proceedings
- 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.
Abstracts
- 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, 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.
Master theses
- J. Spronck, "Multi conditional lung nodule synthesis for improved nodule malignancy classification in Computed Tomography scans", Master thesis, 2020.