DiaMond: Dementia Diagnosis with Multi-Modal Vision Transformers using MRI and PET

Yitong Li, Morteza Ghahremani, Youssef Wally, Christian Wachinger; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 107-116

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. The code is available at https://github.com/ai-med/DiaMond.