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arXiv Machine Learning By Fiona Kekwick, Matthew Baugh, Bernhard Kainz, Paul M. Matthews, Wenjia Bai

MMAP: Multimodal Missing-Aware Pretraining for Longitudinal Alzheimer's Prediction

Read the original on arXiv Machine Learning →

MMAP is a Multimodal Missing‑Aware Alignment Pretraining method designed to learn image‑tabular representations from incomplete data. It uses a sigmoid contrastive learning image encoder with generative reconstruction, a tabular encoder based on a foundation model, and a missing token generator to handle missing modalities. The approach is evaluated on longitudinal Alzheimer’s tasks—predicting disease stage conversion and amyloid status—and outperforms both multimodal and unimodal baselines.

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