Alzheimer's disease and Frontotemporal dementia are common types of neurodegenerative disorders that present overlapping clinical symptoms, making their differential diagnosis very challenging. Numerous efforts have been done for the diagnosis of each disease but the problem of multi-class differential diagnosis has not been actively explored. In recent years, transformer-based models have demonstrated remarkable success in various computer vision tasks. However, their use in disease diagnostic is uncommon due to the limited amount of 3D medical data given the large size of such models. In this paper, we present a novel 3D transformer-based architecture using a deformable patch location module to improve the differential diagnosis of Alzheimer's disease and Frontotemporal dementia. Moreover, to overcome the problem of data scarcity, we propose an efficient combination of various data augmentation techniques, adapted for training transformer-based models on 3D structural magnetic resonance imaging data. Finally, we propose to combine our transformer-based model with a traditional machine learning model using brain structure volumes to better exploit the available data. Our experiments demonstrate the effectiveness of the proposed approach, showing competitive results compared to state-of-the-art methods. Moreover, the deformable patch locations can be visualized, revealing the most relevant brain regions used to establish the diagnosis of each disease.

翻译：阿尔茨海默病和额颞叶痴呆是常见的神经退行性疾病，两者临床表现重叠，使得鉴别诊断极具挑战性。已有大量研究致力于每种疾病的单独诊断，但多类别鉴别诊断问题尚未得到充分探索。近年来，基于Transformer的模型在多种计算机视觉任务中展现出卓越性能。然而，由于此类模型规模庞大而3D医学数据量有限，其在疾病诊断中的应用尚不常见。本文提出一种新型基于3D Transformer的架构，通过可变形补丁定位模块提升阿尔茨海默病与额颞叶痴呆的鉴别诊断能力。此外，为克服数据稀缺问题，我们提出一种针对3D结构磁共振成像数据训练Transformer模型的高效组合数据增强策略。最后，我们通过结合基于脑结构体积的传统机器学习模型与所提出的Transformer模型，以更充分利用现有数据。实验结果表明，该方法具有有效性，其性能可与现有最优方法相媲美。同时，可变形补丁位置的可视化揭示了用于建立每种疾病诊断的最相关脑区。