In this work , we develop a new data - driven deep spectral learning ( DSL ) method to enable highly robust and reliable sO 2 estimation , as shown in Fig . 1b . By training a neural network to directly relate the spectral measurements to the corresponding independent sO 2 labels , DSL bypasses the need for a rigid parametric model , similar to existing deep - learning methods for solving optical inverse problems [ 32][33][34][35][36 ] . We show that DSL can be trained to be highly robust to multiple sources of variabilities in the experiments , including different setups , imaging protocols , speeds , and other possible longitudinal variations .