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

翻译：在多中心随机随机临床试验成像数据中,由于获取技术或扫描程序,可以有多种不同的临床试验成像数据。预测病人未来结果的模型受到这种数据差异的损害。在这里,我们提出一种预测方法,可以应付大量不同扫描地点和每个地点的低样本。我们根据扫描的视觉外观将各地点集中成假域,并培训假基因特定模型。结果显示,从首次访问获得的成像数据以及12周肝病后续调查中获得的成像数据中,这些模型在48周后提高了对胃病的预测准确性。