@Article{biros_et_al:DagRep.13.1.36,
author = {Biros, George and Mang, Andreas and Menze, Bj\"{o}rn H. and Schulte, Miriam},
title = {{Inverse Biophysical Modeling and Machine Learning in Personalized Oncology (Dagstuhl Seminar 23022)}},
pages = {36--67},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2023},
volume = {13},
number = {1},
editor = {Biros, George and Mang, Andreas and Menze, Bj\"{o}rn H. and Schulte, Miriam},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.13.1.36},
URN = {urn:nbn:de:0030-drops-191189},
doi = {10.4230/DagRep.13.1.36},
annote = {Keywords: Bayesian inverse problems, image segmentation, inverse problems, machine learning, medical image analysis, parallel computing, tumor growth simulation and modeling}
}