Multimodal Contrastive Learning and Tabular Attention for Automated Alzheimer's Disease Prediction
This work addresses the challenge of leveraging multimodal data for Alzheimer's disease prediction, offering a domain-specific improvement.
The paper tackled the problem of predicting Alzheimer's disease by integrating neuroimaging and tabular data, achieving an accuracy of 83.8%, which is a nearly 10% increase over previous state-of-the-art methods.
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.