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.

翻译：痴呆症的诊断，特别是阿尔茨海默病（AD）和额颞叶痴呆（FTD），因其症状重叠而变得复杂。虽然磁共振成像（MRI）和正电子发射断层扫描（PET）数据对于诊断至关重要，但在深度学习中整合这些模态面临挑战，其性能通常逊于使用单一模态的方法。此外，在具有重要临床意义的鉴别诊断中，多模态方法的潜力在很大程度上仍未得到探索。我们提出了一种新颖的框架DiaMond，利用视觉Transformer来有效整合MRI和PET数据，以解决这些问题。DiaMond配备了自注意力机制和一种新颖的双注意力机制，能协同结合MRI和PET信息，同时采用多模态归一化以减少冗余依赖，从而提升性能。DiaMond在多个数据集上显著优于现有的多模态方法，在AD诊断中达到92.4%的平衡准确率，在AD-MCI-CN分类中达到65.2%，在AD与FTD的鉴别诊断中达到76.5%。我们还在全面的消融研究中验证了DiaMond的鲁棒性。代码可在 https://github.com/ai-med/DiaMond 获取。