Typical machine learning frameworks heavily rely on an underlying assumption that training
and test data follow the same distribution. In medical imaging which increasingly begun
acquiring datasets from multiple sites or scanners, this identical distribution assumption
often fails to hold due to systematic variability induced by site or scanner dependent factors.
Therefore, we cannot simply expect a model trained on a given dataset to consistently work
well, or generalize, on a dataset from another distribution. In this work, we address this …

Quantification of white matter hyperintensities (WMH) is necessary to understand their role in
several neurological diseases. Measurements such as volumetric estimations need to be
accurate enough to provide valid insights. Consequently, robust and accurate segmentation
of WMH is needed. Modern machine learning techniques such as Deep Learning (DL) have
made important advances in this field, showing unprecedented performance in
segmentation. However, their applicability in realistic clinical scenarios is still questioned …