@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}
}