AINeutralarXiv – CS AI · 9h ago6/10
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Learning Cross-Atlas Consistent Brain Disorder Representations via Disentangled Multi-Atlas Functional Connectivity Learning
Researchers propose MADCLE, a machine learning framework that learns consistent brain disorder representations across multiple brain atlases by disentangling disease-related features from atlas-dependent and covariate factors. The approach demonstrates competitive performance on neurological disorder datasets (ADNI and ADHD-200) while addressing the fundamental problem that different brain parcellation schemes produce heterogeneous and sometimes contradictory functional connectivity representations.