  In AI-based histopathology, domain shifts are common and well-studied.
However, this research focuses on stain and scanner variations, which do not
show the full picture -- shifts may be combinations of other shifts, or
"invisible" shifts that are not obvious but still damage performance of machine
learning models. Furthermore, it is important for models to generalize to these
shifts without expensive or scarce annotations, especially in the
histopathology space and if wanting to deploy models on a larger scale. Thus,
there is a need for "reactive" domain generalization techniques: ones that
adapt to domain shifts at test-time rather than requiring predictions of or
examples of the shifts at training time. We conduct a literature review and
introduce techniques that react to domain shifts rather than requiring a
prediction of them in advance. We investigate test time training, a technique
for domain generalization that adapts model parameters at test-time through
optimization of a secondary self-supervised task.
