Home  | Publications | LGW+25

DiaMond: Dementia Diagnosis With Multi-Modal Vision Transformers Using MRI and PET

MCML Authors

Link to Profile Christian Wachinger PI Matchmaking

Christian Wachinger

Prof. Dr.

Principal Investigator

Artificial Intelligence in Medical Imaging

Abstract

Diagnosing dementia, particularly for Alzheimer's Disease (AD) and frontotemporal dementia (FTD), is complex due to overlapping symptoms. While magnetic resonance imaging (MRI) and positron emission tomography (PET) data are critical for the diagnosis, integrating these modalities in deep learning faces challenges, often resulting in suboptimal performance compared to using single modalities. Moreover, the potential of multi-modal approaches in differential diagnosis, which holds significant clinical importance, remains largely unexplored. We propose a novel framework, DiaMond, to address these issues with vision Transformers to effectively integrate MRI and PET. DiaMond is equipped with self-attention and a novel bi-attention mechanism that synergistically combine MRI and PET, alongside a multi-modal normalization to reduce redundant dependency, thereby boosting the performance. DiaMond significantly outperforms existing multi-modal methods across various datasets, achieving a balanced accuracy of 92.4% in AD diagnosis, 65.2% for AD-MCI-CN classification, and 76.5% in differential diagnosis of AD and FTD. We also validated the robustness of DiaMond in a comprehensive ablation study.

inproceedings

WACV 2025

IEEE/CVF Winter Conference on Applications of Computer Vision. Tucson, AZ, USA, Feb 28-Mar 04, 2025.
Conference logo
A Conference

Authors

Y. Li • M. Ghahremani • Y. Wally • C. Wachinger

Links

DOI

Research Area

 C1 | Medicine

BibTeXKey: LGW+25

Back to Top