  Multispectral optical imaging is becoming a key tool in the operating room.
Recent research has shown that machine learning algorithms can be used to
convert pixel-wise reflectance measurements to tissue parameters, such as
oxygenation. However, the accuracy of these algorithms can only be guaranteed
if the spectra acquired during surgery match the ones seen during training. It
is therefore of great interest to detect so-called out of distribution (OoD)
spectra to prevent the algorithm from presenting spurious results. In this
paper we present an information theory based approach to OoD detection based on
the widely applicable information criterion (WAIC). Our work builds upon recent
methodology related to invertible neural networks (INN). Specifically, we make
use of an ensemble of INNs as we need their tractable Jacobians in order to
compute the WAIC. Comprehensive experiments with in silico, and in vivo
multispectral imaging data indicate that our approach is well-suited for OoD
detection. Our method could thus be an important step towards reliable
functional imaging in the operating room.

Image and Video Processing;Machine Learning;Medical Physics;Machine Learning