License: 
Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/DagRep.13.1.36
URN: urn:nbn:de:0030-drops-191189
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2023/19118/
Biros, George ;
Mang, Andreas ;
Menze, Björn H. ;
Schulte, Miriam
Weitere Beteiligte (Hrsg. etc.): George Biros and Andreas Mang and Björn H. Menze and Miriam Schulte
Inverse Biophysical Modeling and Machine Learning in Personalized Oncology (Dagstuhl Seminar 23022)
Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 23022 "Inverse Biophysical Modeling and Machine Learning in Personalized Oncology".
This seminar brought together leading experts in mathematical, computational, and medical imaging sciences with research interests in data science, scientific machine learning, modeling and numerical simulation, optimization, and statistical and deterministic inversion, and image analysis with applications in medical imaging, and, in particular, oncology. A central theme of the seminar was the integration of data-driven methods with model-driven approaches for predictive modeling.
The seminar had several main thrusts including design and analysis of novel mathematical models, recent developments in medical imaging, machine learning in the context data analytics and data-driven model prediction, predictive computational modeling through (statistical) inversion, integration of machine learning with model-based priors and use of these methods to aid decision-making. We discussed these topics through the lens of foundational algorithmic complications and mathematical and computational challenges. The participants explored how advances in the applied sciences (e.g., data analytics, medical imaging, or radiomics) can aid us to tackle challenges in the application domain. We also discussed the significant challenges associated with the validation of the proposed methodology, and a lack of reproducibility due to the absence of standard protocols for validation of data- and model-driven methods by translational research groups.
BibTeX - Entry
@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/opus/volltexte/2023/19118},
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}
}
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Keywords: |
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Bayesian inverse problems, image segmentation, inverse problems, machine learning, medical image analysis, parallel computing, tumor growth simulation and modeling |
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Collection: |
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DagRep, Volume 13, Issue 1 |
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Issue Date: |
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2023 |
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Date of publication: |
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18.09.2023 |
