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Pseudo-domains in imaging data improve prediction of future disease status in multi-center studies
Matthias Perkonigg · Ahmed Ba-Ssalamah · Georg Langs

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.

Author Information

Matthias Perkonigg (Medical University of Vienna)
Ahmed Ba-Ssalamah (Medical University of Vienna)
Georg Langs (Medical University of Vienna)

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