# Invertible Neural Networks for Uncertainty Quantification in Photoacoustic Imaging

@inproceedings{Nlke2020InvertibleNN, title={Invertible Neural Networks for Uncertainty Quantification in Photoacoustic Imaging}, author={Jan-Hinrich N{\"o}lke and Tim J. Adler and Janek Gr{\"o}hl and Lynton Ardizzone and Carsten Rother and U. K{\"o}the and Lena Maier-Hein}, booktitle={Bildverarbeitung f{\"u}r die Medizin}, year={2020} }

Multispectral photoacoustic imaging (PAI) is an emerging imaging modality which enables the recovery of functional tissue parameters such as blood oxygenation. However, the underlying inverse problems are potentially ill-posed, meaning that radically different tissue properties may - in theory - yield comparable measurements. In this work, we present a new approach for handling this specific type of uncertainty by leveraging the concept of conditional invertible neural networks (cINNs…

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