  Pooled imaging data from multiple sources is subject to bias from each
source. Studies that do not correct for these scanner/site biases at best lose
statistical power, and at worst leave spurious correlations in their data.
Estimation of the bias effects is non-trivial due to the paucity of data with
correspondence across sites, so called "traveling phantom" data, which is
expensive to collect. Nevertheless, numerous solutions leveraging direct
correspondence have been proposed. In contrast to this, Moyer et al. (2019)
proposes an unsupervised solution using invariant representations, one which
does not require correspondence and thus does not require paired images. By
leveraging the data processing inequality, an invariant representation can then
be used to create an image reconstruction that is uninformative of its original
source, yet still faithful to the underlying structure. In the present abstract
we provide an overview of this method.
