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It is observed that MCR 2 can completely fail under distribution shift, motivating the study of its out-of-distribution (OOD) generalisation limits, and two limitations of MCR 2 are established for OOD generalisation.
MAGE is a novel unsupervised anomaly detection framework that integrates multi head memory gating, self-supervised learning, and adversarial training within a unified convolutional autoencoder architecture that consistently outperforms state-of-the-art baselines in standard within-dataset evaluation settings.
DeMMO is proposed, an interpretable framework for longitudinal, multi-disease, and multi-outcome learning that infers signed relations directly from learned longitudinal DMO-outcome mappings, thereby enabling selective information sharing across cohorts without requiring paired participants.
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