Benutzer: Gast  Login
Weniger Felder
Einfache Suche
Titel:

A Learnable Prior Improves Inverse Tumor Growth Modeling.

Dokumenttyp:
Preprint
Autor(en):
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...     »
Zeitschriftentitel:
ArXiv
Jahr:
2024
PubMed:
http://view.ncbi.nlm.nih.gov/pubmed/38495563
TUM Einrichtung:
Institut für KI und Informatik in der Medizin (Prof. Rückert)
 BibTeX