Machine Learning Techniques for MRI Data Processing at Expanding Scale
This work tackles data integration and privacy issues in medical imaging for researchers and clinicians, but it is incremental as it reviews existing methods without introducing new techniques.
The chapter addresses the challenge of distribution shifts in large-scale MRI datasets from multiple studies and explores transfer learning, federated learning, and representation learning as solutions to enable effective machine learning analysis across diverse data sources.
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.