  Many automatic machine learning models developed for focal pathology (e.g.
lesions, tumours) detection and segmentation perform well, but do not
generalize as well to new patient cohorts, impeding their widespread adoption
into real clinical contexts. One strategy to create a more diverse,
generalizable training set is to naively pool datasets from different cohorts.
Surprisingly, training on this \it{big data} does not necessarily increase, and
may even reduce, overall performance and model generalizability, due to the
existence of cohort biases that affect label distributions. In this paper, we
propose a generalized affine conditioning framework to learn and account for
cohort biases across multi-source datasets, which we call Source-Conditioned
Instance Normalization (SCIN). Through extensive experimentation on three
different, large scale, multi-scanner, multi-centre Multiple Sclerosis (MS)
clinical trial MRI datasets, we show that our cohort bias adaptation method (1)
improves performance of the network on pooled datasets relative to naively
pooling datasets and (2) can quickly adapt to a new cohort by fine-tuning the
instance normalization parameters, thus learning the new cohort bias with only
10 labelled samples.
