Sanjay Prabhu

Abstract Artificial intelligence applied to brain magnetic resonance imaging (MRI) holds potential to advance diagnosis, prognosis and treatment planning for neurological diseases. The field has been constrained, thus far, by limited training data and task-specific models that do not generalize well across patient populations and medical tasks. By leveraging self-supervised learning, pretraining and targeted adaptation, foundation models present a promising paradigm to overcome these limitations.

Renal hemosiderosis results from accumulation of hemosiderin in the kidneys. It is typically considered a benign and incidental radiologic finding, and it rarely results in clinically apparent renal dysfunction.