Deep spectral learning for label-free optical imaging oximetry with uncertainty quantification

摘要

Methods for calculating retinal blood vessel O by the traditional LSF and our neural-network-based DSL with uncertainty quantification. Traditional LSF optimizes a rigid parametric model to best fit the spectral measurements for sO . DSL bypasses any rigid models and trains neural networks with paired arterial spectra and oximeter O readings as the ground truth. After training, the neural network models predict both O and its uncertainty.