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- W4319831240 abstract "There has been an increasing interest in utilizing machine learning methods in inverse problems and imaging. Most of the work has, however, concentrated on image reconstruction problems, and the number of studies regarding the full solution of the inverse problem is limited. In this work, we study a machine learning–based approach for the Bayesian inverse problem of photoacoustic tomography. We develop an approach for estimating the posterior distribution in photoacoustic tomography using an approach based on the variational autoencoder. The approach is evaluated with numerical simulations and compared to the solution of the inverse problem using a Bayesian approach." @default.
- W4319831240 created "2023-02-11" @default.
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- W4319831240 date "2023-01-24" @default.
- W4319831240 modified "2023-10-17" @default.
- W4319831240 title "Utilizing Variational Autoencoders in the Bayesian Inverse Problem of Photoacoustic Tomography" @default.
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- W4319831240 doi "https://doi.org/10.1137/22m1489897" @default.
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