Abstract：
In Alzheimer\'s disease (AD) assessment, traditional deep learning approaches have often employed separate methodologies to handle the diverse modalities of input data. Recognizing the critical need for a cohesive and interconnected analytical framework, we propose the AD-Transformer, a novel transformer-based unified deep learning model. This innovative framework seamlessly integrates structural magnetic resonance imaging (sMRI), clinical, and genetic data from the extensive Alzheimer\'s Disease Neuroimaging Initiative (ADNI) database, encompassing 1651 subjects. By employing a Patch-CNN block, the AD-Transformer efficiently transforms image data into image tokens, while a linear projection layer adeptly converts non-image data into corresponding tokens. As the core, a transformer block learns comprehensive representations of the input data, capturing the intricate interplay between modalities. The AD-Transformer sets a new benchmark in AD diagnosis and Mild Cognitive Impairment (MCI) conversion prediction, achieving remarkable average area under curve (AUC) values of 0.993 and 0.845, respectively, surpassing those of traditional image-only models and non-unified multimodal models. Our experimental results confirmed the potential of the AD-Transformer as a potent tool in AD diagnosis and MCI conversion prediction. By providing a unified framework that jointly learns holistic representations of both image and non-image data, the AD-Transformer paves the way for more effective and precise clinical assessments, offering a clinically adaptable strategy for leveraging diverse data modalities in the battle against AD.
摘要：
在阿尔茨海默病(AD)评估中，传统的深度学习方法通常采用单独的方法来处理输入数据的不同模式。认识到迫切需要有凝聚力和相互联系的分析框架，我们提出了AD-Transformer，一种新颖的基于变压器的统一深度学习模型。这种创新的框架无缝集成了结构磁共振成像（sMRI），临床,和广泛的阿尔茨海默病神经影像学倡议（ADNI）数据库中的遗传数据，涵盖1651个主题。通过使用补丁CNN块，AD-Transformer将图像数据有效地转换为图像令牌，而线性投影层巧妙地将非图像数据转换为相应的标记。作为核心,变压器块学习输入数据的综合表示，捕捉模式之间复杂的相互作用。AD-Transformer在AD诊断和轻度认知障碍（MCI）转换预测中树立了新的基准，达到显著的平均曲线下面积（AUC）值分别为0.993和0.845，超越传统的纯图像模型和非统一的多模态模型。我们的实验结果证实了AD-Transformer作为AD诊断和MCI转换预测的有力工具的潜力。通过提供一个统一的框架，共同学习图像和非图像数据的整体表示，AD-Transformer为更有效和精确的临床评估铺平了道路，提供一种临床适应性策略，利用不同的数据模式对抗AD。
