9 papers
Inpainting physics: self-supervised learning for context-driven fluid simulation
Jonas Weidner, Yeray Martin-Ruisanchez, Daniel Rueckert +2
Neural surrogate models for computational fluid dynamics (CFD) are typically trained as forward operators that map explicit problem specifications, such as geometry and boundary co…
TumorFlow: Physics-Guided Longitudinal MRI Synthesis of Glioblastoma Growth
Valentin Biller, Niklas Bubeck, Lucas Zimmer +6
Glioblastoma exhibits diverse, infiltrative, and patient-specific growth patterns that are only partially visible on routine MRI, making it difficult to reliably assess true tumor…
A Biophysically-Conditioned Generative Framework for 3D Brain Tumor MRI Synthesis
Valentin Biller, Lucas Zimmer, Ayhan Can Erdur +4
Magnetic resonance imaging (MRI) inpainting supports numerous clinical and research applications. We introduce the first generative model that conditions on voxel-level, continuous…
Interpretable Retinal Disease Prediction Using Biology-Informed Heterogeneous Graph Representations
Laurin Lux, Alexander H. Berger, Maria Romeo Tricas +8
Interpretability is crucial to enhance trust in machine learning models for medical diagnostics. However, most state-of-the-art image classifiers based on neural networks are not i…
Individualizing Glioma Radiotherapy Planning by Optimization of Data and Physics-Informed Discrete Loss
Michal Balcerak, Jonas Weidner, Petr Karnakov +8
Brain tumor growth is unique to each glioma patient and extends beyond what is visible in imaging scans, infiltrating surrounding brain tissue. Understanding these hidden patient-s…
Efficient Deep Learning-based Forward Solvers for Brain Tumor Growth Models
Zeineb Haouari, Jonas Weidner, Yeray Martin-Ruisanchez +5
Glioblastoma, a highly aggressive brain tumor, poses major challenges due to its poor prognosis and high morbidity rates. Partial differential equation-based models offer promising…