Machine Learning Techniques for MRI Data Processing at Expanding Scale

TL;DR

This work addresses the challenge of deploying machine learning on large-scale MRI datasets across diverse cohorts, where distribution shifts and data governance constrain generalization and sharing. It surveys transfer learning, federated learning, and representation learning as strategies to transfer knowledge, protect privacy, and create robust, multi-modal embeddings. Key examples discussed include nnU-Net and TotalSegmentator for segmentation, CLIP-style language–image pre-training, and universal segmentation models, as well as data harmonization and QC approaches. The work highlights how these methods enable scalable biomarker discovery and cross-cohort analyses, while underscoring remaining hurdles in generalization and deployment.

Abstract

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.