# M²PFN

The work proposes M²PFN, an end-to-end framework that back-propagates task gradients into 3D-MRI and tabular encoders through differentiable inference, aligns the two modalities into a shared subspace matched to the in-context learning prior via disentanglement and a contrastive objective, and folds in a frozen tabular-only prediction through a learnable gated shortcut; on ADNI (n=2240, three-class CN/MCI/AD) it attains 65.55% macro-F1 and 82.21% macro-AUC, surpassing the compared unimodal and multimodal baselines, regresses baseline MMSE by swapping only the head (1250-subject sub-cohort, test MAE 1.743), and achieves the best AUC and lowest MMSE MAE across all baselines on two external cohorts (OASIS-3 and SCAN) with no retraining, transferring even when the cognitive instrument changes.

The work proposes M²PFN, an end-to-end framework that back-propagates task gradients into 3D-MRI and tabular encoders through differentiable inference, aligns the two modalities into a shared subspace matched to the in-context learning prior via disentanglement and a contrastive objective, and folds in a frozen tabular-only prediction through a learnable gated shortcut; on ADNI (n=2240, three-class CN/MCI/AD) it attains 65.55% macro-F1 and 82.21% macro-AUC, surpassing the compared unimodal and multimodal baselines, regresses baseline MMSE by swapping only the head (1250-subject sub-cohort, test MAE 1.743), and achieves the best AUC and lowest MMSE MAE across all baselines on two external cohorts (OASIS-3 and SCAN) with no retraining, transferring even when the cognitive instrument changes.