Keywords: Machine Learning/Artificial Intelligence, Spectroscopy, deep learning, machine learning, convolutional neural network, metabolite quantification, Bayesian neural network, variational autoencoder, epistemic uncertainty, aleatoric uncertainty
While deep learning (DL)-based approaches have been employed to quantify MRS signals, the interpretability of deep-model results and assessing what a DL model knows is a crucial component of DL-based approaches. We present a physics-informed DL-based algorithm for MRS data quantification with simultaneous uncertainty estimation, which uses the advantages of linear combination model fitting and the capabilities of ensembles of variational autoencoders. We acknowledge the need for further investigation with in-vivo datasets and other approaches. Furthermore, a more thorough analysis that includes scores that take estimation variation and uncertainty for both the proposed DL technique and traditional model fitting is required.
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