arXiv:2604.27277v2 Announce Type: replace Abstract: Brain MRI underpins a wide range of neuroscientific and clinical applications, yet most learning-based methods remain task-specific and require substantial labeled data. Here we show that a single self-supervised representation can generalize across heterogeneous brain MRI endpoints. We trained BrainDINO, a self-distilled foundation model, on approximately 6.6 million unlabeled axial slices from 20 datasets encompassing broad variation in population, disease, and acquisition setting. Using a frozen encoder with lightweight task heads, BrainDI
The proliferation of self-supervised learning techniques and large unlabeled datasets is enabling the creation of foundation models specialized for medical imaging.
This development allows for more generalizable and less data-intensive AI applications in clinical diagnostics, potentially accelerating medical research and improving patient care efficiency.
A single, self-supervised representation can now generalize across diverse brain MRI endpoints, reducing the need for extensive, task-specific labeled data for each new application.
- · AI medical imaging companies
- · Healthcare providers
- · Neurology researchers
- · Patients
- · Legacy medical imaging software companies (without AI integration)
- · Small-scale, task-specific AI diagnostic developers
BrainDINO will facilitate faster development and deployment of AI tools for brain disease diagnosis and prognosis.
The cost of developing and deploying advanced brain imaging AI will significantly decrease, democratizing access to sophisticated diagnostic capabilities.
Integrated AI models could lead to new discoveries in neuroscience by identifying subtle patterns across vast and diverse MRI datasets that human analysis might miss.
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Read at arXiv cs.LG