no code implementations • 12 Feb 2024 • Douwe J. Spaanderman, Martijn P. A. Starmans, Gonnie C. M. van Erp, David F. Hanff, Judith H. Sluijter, Anne-Rose W. Schut, Geert J. L. H. van Leenders, Cornelis Verhoef, Dirk J. Grunhagen, Wiro J. Niessen, Jacob J. Visser, Stefan Klein
Next, the method was externally validated on a dataset including five unseen STT phenotypes in extremities, achieving 0. 81$\pm$0. 08 for CT, 0. 84$\pm$0. 09 for T1-weighted MRI, and 0. 88\pm0. 08 for previously unseen T2-weighted fat-saturated (FS) MRI.
2 code implementations • 19 Aug 2021 • Martijn P. A. Starmans, Sebastian R. van der Voort, Thomas Phil, Milea J. M. Timbergen, Melissa Vos, Guillaume A. Padmos, Wouter Kessels, David Hanff, Dirk J. Grunhagen, Cornelis Verhoef, Stefan Sleijfer, Martin J. van den Bent, Marion Smits, Roy S. Dwarkasing, Christopher J. Els, Federico Fiduzi, Geert J. L. H. van Leenders, Anela Blazevic, Johannes Hofland, Tessa Brabander, Renza A. H. van Gils, Gaston J. H. Franssen, Richard A. Feelders, Wouter W. de Herder, Florian E. Buisman, Francois E. J. A. Willemssen, Bas Groot Koerkamp, Lindsay Angus, Astrid A. M. van der Veldt, Ana Rajicic, Arlette E. Odink, Mitchell Deen, Jose M. Castillo T., Jifke Veenland, Ivo Schoots, Michel Renckens, Michail Doukas, Rob A. de Man, Jan N. M. IJzermans, Razvan L. Miclea, Peter B. Vermeulen, Esther E. Bron, Maarten G. Thomeer, Jacob J. Visser, Wiro J. Niessen, Stefan Klein
In this study we propose a framework for automatically optimizing the construction of radiomics workflows per application.
1 code implementation • 14 Oct 2020 • Martijn P. A. Starmans, Milea J. M. Timbergen, Melissa Vos, Michel Renckens, Dirk J. Grünhagen, Geert J. L. H. van Leenders, Roy S. Dwarkasing, François E. J. A. Willemssen, Wiro J. Niessen, Cornelis Verhoef, Stefan Sleijfer, Jacob J. Visser, Stefan Klein
The aim of this study was to evaluate radiomics for distinguishing GISTs from other intra-abdominal tumors, and in GISTs, predict the c-KIT, PDGFRA, BRAF mutational status and mitotic index (MI).
no code implementations • MIDL 2019 • David Tellez, Diederik Hoppener, Cornelis Verhoef, Dirk Grunhagen, Pieter Nierop, Michal Drozdzal, Jeroen van der Laak, Francesco Ciompi
Additionally, we trained multiple encoders with different training objectives, e. g. unsupervised and variants of MTL, and observed a positive correlation between the number of tasks in MTL and the system performance on the TUPAC16 dataset.