文献类型：期刊文献

中文题名：一种基于师生间注意力的AD诊断模型

英文题名：A Model for Teacher-Student Attention Based Alzheimer's Disease Analysis

作者：李宜儒[1];罗健旭[1]

机构：[1]华东理工大学信息科学与工程学院,上海200237

年份：2023

卷号：49

期号：4

起止页码：583

中文期刊名：华东理工大学学报（自然科学版）

外文期刊名：Journal of East China University of Science and Technology

收录：Scopus;北大核心:【北大核心2020】；CSCD:【CSCD_E2023_2024】；

基金：上海市科技创新行动计划(19511121203)。

语种：中文

中文关键词：阿尔兹海默病;小样本;深度学习;知识蒸馏;注意力机制

外文关键词：Alzheimer's disease;small sample;deep learning;knowledge distillation;attention mechanism

摘要：提出了一种卷积神经网络(Convolutional Neural Networks,CNN)和长短期记忆网络(Long Short Term Memory,LSTM)级联的阿尔兹海默病(AD)的诊断模型,先用CNN提取图像的深层特征,再用LSTM对序列特征进行分类,这样将3D的核磁共振成像(MRI)图像视为2D图像的序列,考虑了切片序列间的关联信息。为了提高模型在小样本数据上的性能表现,采用知识蒸馏算法训练轻量级的学生模型,同时引入师生间的注意力机制提高模型分类的准确率。实验表明,该诊断模型在ADNI(Alzheimer's Disease Neuroimaging Initiative)数据集上取得了良好的性能。
The early diagnosis of Alzheimer's disease(AD)through magnetic resonance imaging(MRI)analysis is of great significance.The detection of AD from neuroimaging data such as MRI through deep learning has become an attractive method.However,MRI image analysis based on deep learning often faces challenges such as small training sample size and high 3D data calculation costs.In order to get better model performance on small samples of MRI,this paper proposes a cascaded CNN and LSTM AD diagnosis model.Firstly,CNN is used to extract deep features of the image.Then,LSTM is used to classify sequence features.This treats 3D MRI images as sequences of 2D images,taking into account the correlation information between slice sequences.In addition,in order to improve the performance of the model on small sample data,knowledge distillation algorithms are used to obtain compressed lightweight model,and inter-teacher-student attention mechanism is introduced to improve the accuracy of model classification.The experiment shows that the diagnostic model can achieve better performance on the Alzheimer's disease neuroimaging initiative(ADNI)dataset.