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Standard ComBat harmonization methods fall apart when applied to dMRI data containing neurological disorders, but a simple MLP-based outlier compensation fixes the problem.
Even with 80% of brain scan data missing, ACADiff can accurately generate the missing modalities and maintain robust diagnostic performance for Alzheimer's disease.
