标题： 利用视频视觉变压器从3D脑部MRI进行阿尔茨海默病诊断 显示英文标题

标题： Leveraging Video Vision Transformer for Alzheimer's Disease Diagnosis from 3D Brain MRI

摘要： 阿尔茨海默病（AD）是一种影响全球数百万人的神经退行性疾病，需要早期和准确的诊断以实现最佳患者管理。近年来，深度学习的进步在医学图像分析中显示出显著的潜力。方法 在这项研究中，我们提出了“ViTranZheimer”，一种利用视频视觉变压器分析3D脑部MRI数据的AD诊断方法。通过将3D MRI体积视为视频，我们利用切片之间的时序依赖性来捕捉复杂的结构关系。视频视觉变压器的自注意力机制使模型能够学习长距离依赖关系并识别可能表明AD进展的细微模式。我们提出的深度学习框架旨在提高AD诊断的准确性和敏感性，为临床医生提供一种用于早期检测和干预的工具。我们使用ADNI数据集验证了视频视觉变压器的性能，并与其他相关模型进行了比较分析。结果 所提出的ViTranZheimer模型与两个混合模型CNN-BiLSTM和ViT-BiLSTM进行了比较。CNN-BiLSTM是卷积神经网络（CNN）和双向长短期记忆网络（BiLSTM）的组合，而ViT-BiLSTM是视觉变压器（ViT）与BiLSTM的组合。ViTranZheimer、CNN-BiLSTM和ViT-BiLSTM模型达到的准确率分别为98.6%、96.479%和97.465%。ViTranZheimer在准确率方面达到最高值98.6%，在该评估指标上优于其他模型，表明其在该特定评估指标上的优越性能。结论 这项研究推进了深度学习技术在神经影像学和阿尔茨海默病研究中的应用理解，为更早和更少侵入性的临床诊断铺平了道路。 显示英文摘要

摘要： Alzheimer's disease (AD) is a neurodegenerative disorder affecting millions worldwide, necessitating early and accurate diagnosis for optimal patient management. In recent years, advancements in deep learning have shown remarkable potential in medical image analysis. Methods In this study, we present "ViTranZheimer," an AD diagnosis approach which leverages video vision transformers to analyze 3D brain MRI data. By treating the 3D MRI volumes as videos, we exploit the temporal dependencies between slices to capture intricate structural relationships. The video vision transformer's self-attention mechanisms enable the model to learn long-range dependencies and identify subtle patterns that may indicate AD progression. Our proposed deep learning framework seeks to enhance the accuracy and sensitivity of AD diagnosis, empowering clinicians with a tool for early detection and intervention. We validate the performance of the video vision transformer using the ADNI dataset and conduct comparative analyses with other relevant models. Results The proposed ViTranZheimer model is compared with two hybrid models, CNN-BiLSTM and ViT-BiLSTM. CNN-BiLSTM is the combination of a convolutional neural network (CNN) and a bidirectional long-short-term memory network (BiLSTM), while ViT-BiLSTM is the combination of a vision transformer (ViT) with BiLSTM. The accuracy levels achieved in the ViTranZheimer, CNN-BiLSTM, and ViT-BiLSTM models are 98.6%, 96.479%, and 97.465%, respectively. ViTranZheimer demonstrated the highest accuracy at 98.6%, outperforming other models in this evaluation metric, indicating its superior performance in this specific evaluation metric. Conclusion This research advances the understanding of applying deep learning techniques in neuroimaging and Alzheimer's disease research, paving the way for earlier and less invasive clinical diagnosis.