Breaking the Atlas Barrier: Adaptive Supervoxels for Interpretable Alzheimer's Disease Diagnosis on PET

Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the leading cause of dementia worldwide. Positron emission tomography (PET) reveals metabolic and molecular abnormalities whose location, extent, and spatial pattern vary substantially across individuals. Existing learning-based methods nevertheless aggregate evidence over predefined slices, patches, or atlas regions that are shared by all subjects. These fixed units misalign with individual abnormalities: they fragment coherent abnormal patterns or mix them with preserved tissue, which limits diagnostic accuracy and makes region-level explanations unreliable when abnormal uptake spans or falls between anatomical boundaries. We propose ASNet, an end-to-end framework that learns the spatial units themselves for PET-based AD diagnosis. Its Adaptive Supervoxel Partitioning (ASP) module learns differentiable voxel-to-supervoxel associations from PET features and spatial coordinates and iteratively refines them for each subject without any atlas. Its Abnormality Evidence Classifier (AEC) measures how far each learned region deviates from a cognitively normal reference and uses this deviation to route the region to an abnormality expert or a preservation expert. The class logits are the exact sum of the resulting region-level contributions, so the explanation is the diagnostic computation itself rather than a post hoc approximation. Across ADNI and AIBL on FDG, amyloid, and tau PET, ASNet attains the highest accuracy in all eight settings and an AUC of up to 96.2%. Substituting fixed atlases for the learned partition lowers AUC by over 10 points, and visual analyses confirm subject-specific, region-level evidence from the learned supervoxels.