  Imaging sites around the world generate growing amounts of medical scan data
with ever more versatile and affordable technology. Large-scale studies acquire
MRI for tens of thousands of participants, together with metadata ranging from
lifestyle questionnaires to biochemical assays, genetic analyses and more.
These large datasets encode substantial information about human health and hold
considerable potential for machine learning training and analysis. This chapter
examines ongoing large-scale studies and the challenge of distribution shifts
between them. Transfer learning for overcoming such shifts is discussed,
together with federated learning for safe access to distributed training data
securely held at multiple institutions. Finally, representation learning is
reviewed as a methodology for encoding embeddings that express abstract
relationships in multi-modal input formats.
