  In multi-center randomized clinical trials imaging data can be diverse due to
acquisition technology or scanning protocols. Models predicting future outcome
of patients are impaired by this data heterogeneity. Here, we propose a
prediction method that can cope with a high number of different scanning sites
and a low number of samples per site. We cluster sites into pseudo-domains
based on visual appearance of scans, and train pseudo-domain specific models.
Results show that they improve the prediction accuracy for steatosis after 48
weeks from imaging data acquired at an initial visit and 12-weeks follow-up in
liver disease
