4 citations · 8 across the 5 of their papers we have counts for
5 papers
A Benchmark of (MRI-) Foundation Models to Predict IDH Mutational Status in Glioma
Nathan Hollet, Elise Robinson, Efthymios Georgiou +3
Non-invasive prediction of glioma molecular status from routine magnetic resonance imaging (MRI) has shown promising performance, but model generalization remains challenging given…
Mechanistic Learning with Guided Diffusion Models to Predict Spatio-Temporal Brain Tumor Growth
Daria Laslo, Efthymios Georgiou, Marius George Linguraru +4
Predicting the spatio-temporal progression of brain tumors is essential for guiding clinical decisions in neuro-oncology. We propose a hybrid mechanistic learning framework that co…
A review of mechanistic learning in mathematical oncology
John Metzcar, Catherine R. Jutzeler, Paul Macklin +2
Mechanistic learning, the synergistic combination of knowledge-driven and data-driven modeling, is an emerging field. In particular, in mathematical oncology, the application of ma…
Interpretability Aware Model Training to Improve Robustness against Out-of-Distribution Magnetic Resonance Images in Alzheimer's Disease Classification
Merel Kuijs, Catherine R. Jutzeler, Bastian Rieck +1
Owing to its pristine soft-tissue contrast and high resolution, structural magnetic resonance imaging (MRI) is widely applied in neurology, making it a valuable data source for ima…
Image analysis for Alzheimer's disease prediction: Embracing pathological hallmarks for model architecture design
Sarah C. Brüningk, Felix Hensel, Catherine R. Jutzeler +1
Alzheimer's disease (AD) is associated with local (e.g. brain tissue atrophy) and global brain changes (loss of cerebral connectivity), which can be detected by high-resolution str…