Uncertainty-aware deep learning in multispectral optical and photoacoustic imaging (Conference Presentation)

Abstract

Optical imaging for estimating physiological parameters, such as tissue oxygenation or blood volume fraction has been an active field of research for many years. In this context, machine learning -based approaches are gaining increasing attention in the literature. Following up on this trend, this talk will present recent progress in multispectral optical and photoacoustic image analysis using deep learning (DL). From a methodological point of view, it will focus on two challenges: (1) How to train a DL algorithm in the absence of reliable reference training data and (2) how to quantify and compensate the different types of uncertainties associated with the inference of physiological parameters. The research presented is being conducted in the scope of the European Research Council (ERC) starting grant COMBIOSCOPY.