Publications of Max de Grauw

2026

Papers in conference proceedings

1. R. Weber, N. Rocholl, M. de Grauw, M. Prokop, E. Smit and A. Hering, "ULS+: Data-driven Model Adaptation Enhances Lesion Segmentation", 2026.
   arXiv

2025

Papers in international journals

1. 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.
   Abstract DOI PMID

2024

Preprints

1. M. de Grauw, E. Scholten, E. Smit, M. Rutten, M. Prokop, B. van Ginneken and A. Hering, "The ULS23 Challenge: a Baseline Model and Benchmark Dataset for 3D Universal Lesion Segmentation in Computed Tomography", arXiv:2406.05231, 2024.
   Abstract DOI PMID arXiv

2022

Abstracts

1. M. Grauw and B. Ginneken, "Semi-supervised 3D universal lesion segmentation in CT thorax-abdomen scans", European Congress of Radiology, 2022.
   Abstract
2. M. Grauw, B. Ginneken, B. Geisler, E. Smit, M. Rooij, S. Schalekamp and M. Prokop, "Deep learning universal lesion segmentation for automated RECIST measurements on CT: comparison to manual assessment by radiologists", European Congress of Radiology, 2022.
   Abstract