@article {DHAMALA2020101670,
	title = {Embedding high-dimensional Bayesian optimization via generative modeling: Parameter personalization of cardiac electrophysiological models},
	journal = {Medical Image Analysis},
	volume = {62},
	year = {2020},
	pages = {101670},
	publisher = {Elsevier},
	abstract = {The estimation of patient-specific tissue properties in the form of model parameters is important for personalized physiological models. Because tissue properties are spatially varying across the underlying geometrical model, it presents a significant challenge of high-dimensional (HD) optimization at the presence of limited measurement data. A common solution to reduce the dimension of the parameter space is to explicitly partition the geometrical mesh. In this paper, we present a novel concept that uses a generative variational auto-encoder (VAE) to embed HD Bayesian optimization into a low-dimensional (LD) latent space that represents the generative code of HD parameters. We further utilize VAE-encoded knowledge about the generative code to guide the exploration of the search space. The presented method is applied to estimating tissue excitability in a cardiac electrophysiological model in a range of synthetic and real-data experiments, through which we demonstrate its improved accuracy and substantially reduced computational cost in comparison to existing methods that rely on geometry-based reduction of the HD parameter space.},
	keywords = {High-dimensional Bayesian optimization, personalized modeling, variational autoencoder},
	issn = {1361-8415},
	doi = {https://doi.org/10.1016/j.media.2020.101670},
	url = {http://www.sciencedirect.com/science/article/pii/S1361841520300360},
	author = {Dhamala, Jwala and Bajracharya, Pradeep and Arevalo, Hermenegild and John L. {Sapp} and Hor{\'a}cek, Milan and Katherine C. {Wu} and Natalia A. {Trayanova} and Wang, Linwei}
}
