# Multimodal Contrastive Learning and Tabular Attention for Automated Alzheimer's Disease Prediction

arXiv: https://arxiv.org/abs/2308.15469 · HTML: https://mlsanity.com/paper/2308.15469/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 31 references that are in the map.
- First posted on arXiv 29 Aug 2023.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Tabular Data: Deep Learning is Not All You Need](https://mlsanity.com/paper/2106.03253/) (2021)
- [Deep Neural Networks and Tabular Data: A Survey](https://mlsanity.com/paper/2110.01889/) (2021)
- [Unified Contrastive Learning in Image-Text-Label Space](https://mlsanity.com/paper/2204.03610/) (2022)
- [Best of Both Worlds: Multimodal Contrastive Learning with Tabular and Imaging Data](https://mlsanity.com/paper/2303.14080/) (2023)
- [Combining 3D Image and Tabular Data via the Dynamic Affine Feature Map Transform](https://mlsanity.com/paper/2107.05990/) (2021)
- [Multimodal Attention-based Deep Learning for Alzheimer's Disease Diagnosis](https://mlsanity.com/paper/2206.08826/) (2022)

## Built on by (most cited first, 12 of 12)

- [A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)](https://mlsanity.com/paper/2404.00579/) (2024)
- [Representation Learning for Tabular Data: A Comprehensive Survey](https://mlsanity.com/paper/2504.16109/) (2025)
- [TIME: TabPFN-Integrated Multimodal Engine for Robust Tabular-Image Learning](https://mlsanity.com/paper/2506.00813/) (2025)
- [TabKAN: Advancing Tabular Data Analysis using Kolmogorov-Arnold Network](https://mlsanity.com/paper/2504.06559/) (2025)
- [Multimodal Representation Learning using Adaptive Graph Construction](https://mlsanity.com/paper/2410.06395/) (2024)
- [Enhancing Multimodal Medical Image Classification using Cross-Graph Modal Contrastive Learning](https://mlsanity.com/paper/2410.17494/) (2024)
- [Code and Pixels: Multi-Modal Contrastive Pre-training for Enhanced Tabular Data Analysis](https://mlsanity.com/paper/2501.07304/) (2025)
- [AMF-MedIT: An Efficient Align-Modulation-Fusion Framework for Medical Image-Tabular Data](https://mlsanity.com/paper/2506.19439/) (2025)
- [Parameter-Efficient Adapter Tuning for Tabular-Image Multimodal Learning](https://mlsanity.com/paper/2606.11682/) (2026)
- [The Importance of Encoder Choice:A Tabular-Image Study](https://mlsanity.com/paper/2607.07756/) (2026)
- [Anatomical Grounding and Leakage-Aware Multimodal Contrastive Learning for Alzheimer's Disease Classification from Structural MRI](https://mlsanity.com/paper/2609.15888/) (2026)
- [MMAP: Multimodal Missing-Aware Pretraining for Longitudinal Alzheimer's Prediction](https://mlsanity.com/paper/2609.26617/) (2026)

## Abstract (arXiv)

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.