Multimodal Contrastive Learning and Tabular Attention for Automated
  Alzheimer's Disease Prediction
Weichen Huang
Alongside neuroimaging such as MRI scans and PET, Alzheimer's disease (AD)
datasets contain valuable tabular data including AD biomarkers and clinical
assessments. Existing computer vision approaches struggle to utilize this
additional information. To address these needs, we propose a generalizable
framework for multimodal contrastive learning of image data and tabular data, a
novel tabular attention module for amplifying and ranking salient features in
tables, and the application of these techniques onto Alzheimer's disease
prediction. Experimental evaulations demonstrate the strength of our framework
by detecting Alzheimer's disease (AD) from over 882 MR image slices from the
ADNI database. We take advantage of the high interpretability of tabular data
and our novel tabular attention approach and through attribution of the
attention scores for each row of the table, we note and rank the most
predominant features. Results show that the model is capable of an accuracy of
over 83.8%, almost a 10% increase from previous state of the art.