Towards generalisable foundation models for brain MRI

Foundation models trained with self-supervised learning are increasingly enabling scalable and generalisable solutions in medical imaging. However, most existing foundation models are designed for 2D natural images and do not explicitly leverage the structure of brain MRI data. We introduce BrainFound, a self-supervised foundation model for brain MRI that adopts a slice-based learning strategy, processing MRI volumes as sequences of 2D slices. This approach enables efficient learning while capturing contextual information across slices. The framework supports both single-modality and multimodal inputs (e.g., T1, T2, FLAIR), allowing integration of complementary structural information. We evaluate BrainFound across multiple downstream tasks, including neurodegenerative disease detection, tumour grading, and brain tissue segmentation, using diverse public datasets. The model consistently outperforms both supervised and self-supervised baselines, particularly in label-scarce and cross-dataset settings, demonstrating strong generalisation. These results highlight the effectiveness of slice-based self-supervised learning as a scalable approach for brain MRI analysis. BrainFound provides a flexible foundation for neuroimaging applications with potential for both clinical deployment and large-scale research.