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A Learnable Prior Improves Inverse Tumor Growth Modeling
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
Informatics 31 - Chair of Artificial Intelligence in Healthcare and Medicine
Technical University of Munich
University of Zurich
Harvard John A. Paulson School of Engineering and Applied Sciences
Helmholtz Zentrum München German Research Center for Environmental Health
University of California, Irvine
University of Manchester
Imperial College London
Research output
:
Contribution to journal
›
Article
›
peer-review
2
Scopus citations
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Keyphrases
Deep Learning Methods
100%
Tumor Growth Modeling
100%
Deep Learning
50%
Model-based Approach
50%
Computational Requirements
50%
Magnetic Resonance Imaging
50%
Partial Differential Equations
50%
Disease Treatment
50%
Parameter Space
50%
Convergence Rate
50%
Dice Score
50%
Sampling Parameters
50%
Evolution Strategy
50%
Cell Concentration
50%
Effective Sampling
50%
Treatment Protocol
50%
Biophysical Modeling
50%
Brain Cancer Cells
50%
Inverse Problem Solving
50%
Ensemble Deep Learning
50%
Deep Learning Prior
50%
Initial Parameter Estimation
50%
Evolutionary Sampling
50%
Earth and Planetary Sciences
Inverse Problem
100%
Parameter Estimation
100%
Growth Modeling
100%
Ensemble Learning
100%
Mathematics
Deep Learning Method
100%
Parameter Space
20%
Dice
20%
Partial Differential Equation
20%
Parameter Estimation
20%
Magnetic Resonance Imaging
20%
Individual Patient
20%
Computer Science
Deep Learning Method
100%
Inverse Problem
20%
Parameter Estimation
20%
Evolution Strategy
20%
Parameter Space
20%
Individual Patient
20%
Partial Differential Equation
20%
Neuroscience
Magnetic Resonance Imaging
100%
Intracranial Tumor
100%
Biochemistry, Genetics and Molecular Biology
Tumor Progression
100%