  Image reconstruction in optoacoustic tomography (OAT) is a trending learning
task highly dependent on measured physical magnitudes present at sensing time.
The large number of different settings, and also the presence of uncertainties
or partial knowledge of parameters, can lead to reconstructions algorithms that
are specifically tailored and designed to a particular configuration which
could not be the one that will be ultimately faced in a final practical
situation. Being able to learn reconstruction algorithms that are robust to
different environments (e.g. the different OAT image reconstruction settings)
or invariant to such environments is highly valuable because it allows to focus
on what truly matters for the application at hand and discard what are
considered spurious features. In this work we explore the use of deep learning
algorithms based on learning invariant and robust representations for the OAT
inverse problem. In particular, we consider the application of the ANDMask
scheme due to its easy adaptation to the OAT problem. Numerical experiments are
conducted showing that, when out-of-distribution generalization (against
variations in parameters such as the location of the sensors) is imposed, there
is no degradation of the performance and, in some cases, it is even possible to
achieve improvements with respect to standard deep learning approaches where
invariance robustness is not explicitly considered.
