Simple domain adaptation for cross-dataset analyses of brain MRI data

We consider the problem of domain shift in analyses of brain MRI data. While many different datasets are publicly available, most algorithms are still trained on a single dataset and often suffer the problem of limited and unbalanced sample sizes. In this work, we propose a surprisingly simple strategy to reduce the impact of domain shift - caused by different data sources and processing pipelines - that typically occurs in cross-dataset analyses. We experimentally evaluate our approach on the problem of using volumetric features to distinguish neurodegenerative diseases and report results using three datasets in two practically relevant scenarios: (1) cross-dataset learning and (2) leveraging pre-trained classifiers across different datasets. We show that our adaptation technique enables both scenarios with performance close to the single-dataset case.