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Title:

A Learnable Prior Improves Inverse Tumor Growth Modeling.

Document type:
Preprint
Author(s):
Weidner, Jonas; Ezhov, Ivan; Balcerak, Michal; Metz, Marie-Christin; Litvinov, Sergey; Kaltenbach, Sebastian; Feiner, Leonhard; Lux, Laurin; Kofler, Florian; Lipkova, Jana; Latz, Jonas; Rueckert, Daniel; Menze, Bjoern; Wiestler, Benedikt
Abstract:
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 syner...     »
Journal title abbreviation:
ArXiv
Year:
2024
Pubmed ID:
http://view.ncbi.nlm.nih.gov/pubmed/38495563
TUM Institution:
Institut für KI und Informatik in der Medizin (Prof. Rückert)
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