3 papers
eess.IV2024
Unraveling Radiomics Complexity: Strategies for Optimal Simplicity in Predictive Modeling
Mahdi Ait Lhaj Loutfi, Teodora Boblea Podasca, Alex Zwanenburg +18
Background: The high dimensionality of radiomic feature sets, the variability in radiomic feature types and potentially high computational requirements all underscore the need for…
cs.CV2023
Graph data modelling for outcome prediction in oropharyngeal cancer patients
Nithya Bhasker, Stefan Leger, Alexander Zwanenburg +4
Graph neural networks (GNNs) are becoming increasingly popular in the medical domain for the tasks of disease classification and outcome prediction. Since patient data is not readi…
cs.CV2018
Assessing robustness of radiomic features by image perturbation
Alex Zwanenburg, Stefan Leger, Linda Agolli +4
Image features need to be robust against differences in positioning, acquisition and segmentation to ensure reproducibility. Radiomic models that only include robust features can b…