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

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

    Measurement of blood oxygen saturation ( s O 2 ) by optical imaging oximetry provides invaluable insight into local tissue functions and metabolism. Despite different embodiments and modalities, all label-free optical-imaging oximetry techniques utilize the same principle of s O 2 -dependent spectral contrast from haemoglobin. Traditional approaches for quantifying s O 2 often rely on analytical models that are fitted by the spectral measurements. These approaches in practice suffer from uncertainties due to biological variability, tissue geometry, light scattering, systemic spectral bias, and variations in the experimental conditions. Here, we propose a new data-driven approach, termed deep spectral learning (DSL), to ...

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