Machine learning-based approaches have shown promise for quantitative photoacoustic oximetry, however, the impact of learned methods is hampered by challenges of usability and generalisability, caused by the strong dependence of learned methods on the training data sets. To address these issues we developed a deep learning-based approach with higher flexibility. The method is trained on a suite of training data sets representing a range of general assumptions. The performance is systematically compared to linear unmixing methods and is validated on in silico, in vitro, and in vivo data representing different use cases.
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