arXiv:2606.29200v1 Announce Type: new Abstract: Multi-site functional MRI (fMRI) studies are essential for robust neuropsychiatric diagnosis yet suffer severe domain shifts from scanner heterogeneity, demographics, and site-specific acquisition protocols. Traditional domain adaptation requires concurrent source and target data access, violating clinical privacy regulations. Moreover, functional connectivity matrices lie on the Symmetric Positive Definite (SPD) manifold, where Euclidean operations cause geometric distortions corrupting diagnostic patterns. We propose BrainRiem, a source-free do
The proliferation of multi-site fMRI studies combined with increasing data privacy concerns makes source-free domain adaptation for medical AI a critical and timely challenge.
This development addresses a significant bottleneck in medical AI by enabling robust diagnostic models across diverse datasets without compromising patient privacy, potentially accelerating neuroscientific research and clinical applications.
The ability to develop and deploy AI models for brain network diagnosis across different institutions without sharing raw patient data or concurrent access to source and target data represents a fundamental change in medical AI methodology.
- · Neuroscience researchers
- · Medical AI developers
- · Hospitals and clinics
- · Patients needing neuropsychiatric diagnoses
- · Traditional domain adaptation techniques
- · AI models reliant on centralized, shared datasets
Improved accuracy and generalizability of AI-powered brain diagnosis tools across different healthcare providers.
Accelerated development and adoption of AI in other sensitive medical imaging applications due to the privacy-preserving methodology.
Potential for new global standards in medical AI development that prioritize data privacy and interoperability through decentralized learning paradigms.
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Read at arXiv cs.LG