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- W3137491541 abstract "Mathematical models of tumor growth and the effect of therapy is fundamental for personalizing and optimizing anticancer therapies. The aim of the research is to provide a good estimation of personalized tumor model parameters based on measurements carried out on the patient. We use in silico experiments to create a large set of training data in a span that covers real-life scenarios. The data are used to train neural networks which provide a good initial guess for the model parameters. The estimated parameters can be used as initial estimations for more sophisticated, but local identification algorithms." @default.
- W3137491541 created "2021-03-29" @default.
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- W3137491541 date "2021-01-21" @default.
- W3137491541 modified "2023-09-27" @default.
- W3137491541 title "Parameter identification of a tumor model using artificial neural networks" @default.
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- W3137491541 doi "https://doi.org/10.1109/sami50585.2021.9378639" @default.
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