Minimally Interactive Segmentation of Soft-Tissue Tumors on CT and MRI using Deep Learning

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.

Interactive Segmentation Segmentation

Reproducible radiomics through automated machine learning validated on twelve clinical applications

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.

Bayesian Optimization BIG-bench Machine Learning

Differential diagnosis and molecular stratification of gastrointestinal stromal tumors on CT images using a radiomics approach

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).

Automated Detection of Cribriform Growth Patterns in Prostate Histology Images

no code implementations • 23 Mar 2020 • Pierre Ambrosini, Eva Hollemans, Charlotte F. Kweldam, Geert J. L. H. van Leenders, Sjoerd Stallinga, Frans Vos

In conclusion, the proposed deep learning method has high sensitivity for detecting cribriform growth patterns at the expense of a limited number of false positives.

Decision Making Ensemble Learning