Defesa de Dissertação de Mestrado: Gaussian Process Modeling with Applications to Tumor Growth

Orientadores:
Regina Célia Cerqueira de Almeida - Laboratório Nacional de Computação Científica - LNCC
Renato Simões Silva - Laboratório Nacional de Computação Científica - LNCC

Banca Examinadora:
Regina Célia Cerqueira de Almeida - Laboratório Nacional de Computação Científica - LNCC (presidente)
Antônio Tadeu Azevedo Gomes - Laboratório Nacional de Computação Científica - LNCC
Pablo Javier Blanco - Laboratório Nacional de Computação Científica - LNCC
Álvaro Luiz Gayoso de Azevedo Coutinho - COPPE/UFRJ - UFRJ
Helcio Rangel Orlande - COPPE/UFRJ - UFRJ

Resumo:

Mechanistic tumor growth models have been used to provide a better understanding of how the disease evolves and help the development of therapies. A side from simulating a certain tumor growth model, it is fundamental to infer its parameters using available experimental data. This process incurs in the solution of an inverse problem, which requires repeated model evaluation, particularly using Bayesian inference. This may be computationally prohibitive for complex models, specially multiscale models. There are techniques of performing approximate Bayesian inference, which reduce the overall computational time, although these may still be infeasible if the model is expensive to be evaluated. In order to alleviate the difficulty in this procedure, we propose the use of a surrogate (or metamodel). Our surrogate model is a Gaussian Process, a data-driven model recently used in machine learning and metamodeling context. This work reviews Gaussian processes (GP) theoretical and practical aspects in a regression problem, discussing how to construct an adequate GP for a particular problem, covering recent developments in this area. We then define a GP surrogate model and combine it with an approximate bayesian computation method, the ABC-MCMC method for solving the inverse problem. We compare the standard and proposed approaches in two tumor growth models. Results suggest that the strategy is promising in terms of reducing the computational cost. Moreover, we comment on how to adaptively construct the surrogate, so as to improve even further the overall process efficiency.

Para assistir acesse: https://us02web.zoom.us/j/84833502639?pwd=M1kwejZ3TVdna2tac0xmZmxzckt0dz09