Multi-View Imputation and Cross-Attention Network Based on Incomplete Longitudinal and Multi-Modal Data for Alzheimer's Disease Prediction. (arXiv:2206.08019v1 [eess.IV])

Web: http://arxiv.org/abs/2206.08019

Longitudinal variations and complementary information inherent in
longitudinal and multi-modal data play an important role in Alzheimer's disease
(AD) prediction, particularly in identifying subjects with mild cognitive
impairment who are about to have AD. However, longitudinal and multi-modal data
may have missing data, which hinders the effective application of these data.
Additionally, previous longitudinal studies require existing longitudinal data
to achieve prediction, but AD prediction is expected to be conducted at
patients' baseline visit (BL) in clinical practice. Thus, we …

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