AI in Medicine

Multimodal Contrastive Learning for Alzheimer’s Disease Prediction in Imaging Genetics

Authors

• • Jonas Fallmann fallmann@ml.jku.at Email Jonas Fallmann. (compose email, opens in email app.)
  • Johannes Kepler Universität
• • Erich Kobler
  • Johannes Kepler Universität

Abstract

Alzheimer’s disease (AD) is an inherently multimodal pathology driven by complex genetic and phenotypic interactions, making reliable early detection a critical challenge. Existing multimodal approaches often struggle to effectively align static baseline genetic risk with longitudinal physical changes. In this work, we introduce a novel two-stage contrastive learning framework integrating Single Nucleotide Polymorphisms (SNPs) and structural MRI volumes. To overcome the bottleneck of single-timepoint genetic measurements, we propose an age-conditioned augmentation strategy that generates time-aware genetic embeddings for longitudinal contrastive pairing. Utilizing a dynamic Gated Fusion mechanism for downstream classification, our approach effectively weights modality contributions. Evaluated on the ADNI database, our framework consistently outperforms strong classical baselines and state-of-the-art generative models, demonstrating particularly significant improvements in early-stage cognitive decline detection.

Keywords:

How to Cite: Fallmann, J. & Kobler, E. (2026) “Multimodal Contrastive Learning for Alzheimer’s Disease Prediction in Imaging Genetics”, Proceedings of the Austrian Symposium on AI, Robotics, and Vision. 3(1). doi: https://doi.org/10.34749/3061-1466.2026.11