Multimodal medical imaging integrates diverse data types, such as structural and functional neuroimaging, to provide complementary insights that enhance deep learning predictions and improve outcomes. This study focuses on a neuroimaging prediction framework based on both structural and functional neuroimaging data. We propose a next-generation prediction model, \textbf{MultiViT2}, which combines a pretrained representative learning base model with a vision transformer backbone for prediction output. Additionally, we developed a data augmentation module based on the latent diffusion model that enriches input data by generating augmented neuroimaging samples, thereby enhancing predictive performance through reduced overfitting and improved generalizability. We show that MultiViT2 significantly outperforms the first-generation model in schizophrenia classification accuracy and demonstrates strong scalability and portability.
@article{arxiv.2506.13667,
title = {MultiViT2: A Data-augmented Multimodal Neuroimaging Prediction Framework via Latent Diffusion Model},
author = {Bi Yuda and Jia Sihan and Gao Yutong and Abrol Anees and Fu Zening and Calhoun Vince},
journal= {arXiv preprint arXiv:2506.13667},
year = {2025}
}