A Learnable Prior Improves Inverse Tumor Growth Modeling
<|reference_start|>A Learnable Prior Improves Inverse Tumor Growth Modeling: Biophysical modeling, particularly involving partial differential equations (PDEs), offers significant potential for tailoring disease treatment protocols to individual patients. However, the inverse problem-solving aspect of these models presents a substantial challenge, either due to the high computational requirements of model-based approaches or the limited robustness of deep learning (DL) methods. We propose a novel framework that leverages the unique strengths of both approaches in a synergistic manner. Our method incorporates a DL ensemble for initial parameter estimation, facilitating efficient downstream evolutionary sampling initialized with this DL-based prior. We showcase the effectiveness of integrating a rapid deep-learning algorithm with a high-precision evolution strategy in estimating brain tumor cell concentrations from magnetic resonance images. The DL-Prior plays a pivotal role, significantly constraining the effective sampling-parameter space. This reduction results in a fivefold convergence acceleration and a Dice-score of 95%<|reference_end|>
@article{weidner2024a,
    title={A Learnable Prior Improves Inverse Tumor Growth Modeling},
    author={Jonas Weidner, Ivan Ezhov, Michal Balcerak, Marie-Christin Metz,
  Sergey Litvinov, Sebastian Kaltenbach, Leonhard Feiner, Laurin Lux, Florian
  Kofler, Jana Lipkova, Jonas Latz, Daniel Rueckert, Bjoern Menze, Benedikt
  Wiestler},
    journal={arXiv preprint arXiv:2403.04500},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.04500},
    primaryClass={physics.med-ph cs.AI}
}