MIT researchers have developed a system that gleans far more labeled training data from unlabeled data, which could help machine-learning models better detect structural patterns in brain scans associated with neurological diseases. The system learns structural and appearance variations in unlabeled scans, and uses that information to shape and mold one labeled scan into thousands of new, distinct labeled scans. System helps machine-learning models glean training information for diagnosing and treating brain conditions. MIT researchers have devised a novel method to glean more information from images used to train machine-learning models, including those that can analyse medical scans to help diagnose and treat brain conditions. An active new area in medicine involves training deep-learning models to detect structural patterns in brain scans associated with neurological diseases and disorders, such as Alzheimer's disease and multiple sclerosis.