基于深度学习的阿尔兹海默症影像学分类研究进展  

Research Progress on Imaging Classification of Alzheimer’s Disease Based on Deep Learning

作　　者：钱程一 王远军[1] QIAN Chengyi;WANG Yuanjun(Institute of Medical Imaging Technology,University of Shanghai for Science and Technology,Shanghai 200093,China)

机构地区：[1]上海理工大学医学影像技术研究所,上海200093

出　　处：《波谱学杂志》2023年第2期220-238,共19页Chinese Journal of Magnetic Resonance

基　　金：上海市自然科学基金资助项目(18ZR1426900).

摘　　要：随着全球老龄化的加剧与深度学习的发展,基于深度学习的阿尔兹海默症(AD)影像学分类成为当前的一个研究热点.本文首先阐述了AD影像学分类任务中常用的深度学习模型、评估标准及公开数据集;接着讨论了不同图像模态在AD影像学分类中的应用;然后着重探讨了应用于AD影像学分类的深度学习模型改进方法;进一步引入了对模型可解释性研究的探讨;最后总结并比较了文中提及的分类模型,归纳了与AD影像分类相关的大脑区域,并对该领域未来的研究方向进行了展望.As global aging worsens and deep learning advances,the imaging classification of Alzheimer’s disease(AD)based on deep learning has become a hot topic of research.This paper reviewed the common deep learning models,evaluation criteria and public datasets in AD imaging classification tasks,discussed the application of different image modalities in AD imaging classification.The content was focused on the improvement of deep learning models applied to AD imaging classification.The studies of model interpretability were also introduced.Finally,the paper summarized and compared the classification models mentioned,identified the brain regions related to AD image classification,and outlined the future research directions in this field.

关 键 词：阿尔茨海默症(AD) 深度学习 医学影像 分类 可解释性 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]