Deep Multi-Modal Discriminative and Interpretability Network for Alzheimer’s Disease Diagnosis

判别式 可解释性 人工智能 计算机科学 典型相关 模式识别(心理学) 深度学习 特征提取 机器学习 特征(语言学) 卷积神经网络 代表(政治) 特征学习 法学 哲学 政治 语言学 政治学
作者
Qi Zhu,Bingliang Xu,Jiashuang Huang,Heyang Wang,Ruting Xu,Wei Shao,Daoqiang Zhang
出处
期刊:IEEE Transactions on Medical Imaging [Institute of Electrical and Electronics Engineers]
卷期号:42 (5): 1472-1483 被引量:32
标识
DOI:10.1109/tmi.2022.3230750
摘要

Multi-modal fusion has become an important data analysis technology in Alzheimer's disease (AD) diagnosis, which is committed to effectively extract and utilize complementary information among different modalities. However, most of the existing fusion methods focus on pursuing common feature representation by transformation, and ignore discriminative structural information among samples. In addition, most fusion methods use high-order feature extraction, such as deep neural network, by which it is difficult to identify biomarkers. In this paper, we propose a novel method named deep multi-modal discriminative and interpretability network (DMDIN), which aligns samples in a discriminative common space and provides a new approach to identify significant brain regions (ROIs) in AD diagnosis. Specifically, we reconstruct each modality with a hierarchical representation through multilayer perceptron (MLP), and take advantage of the shared self-expression coefficients constrained by diagonal blocks to embed the structural information of inter-class and the intra-class. Further, the generalized canonical correlation analysis (GCCA) is adopted as a correlation constraint to generate a discriminative common space, in which samples of the same category gather while samples of different categories stay away. Finally, in order to enhance the interpretability of the deep learning model, we utilize knowledge distillation to reproduce coordinated representations and capture influence of brain regions in AD classification. Experiments show that the proposed method performs better than several state-of-the-art methods in AD diagnosis.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
大个应助超级飞侠采纳,获得50
2秒前
3秒前
st_xp3发布了新的文献求助10
3秒前
5秒前
lailai完成签到,获得积分10
6秒前
Fairyvivi发布了新的文献求助10
6秒前
6秒前
6秒前
7秒前
yina完成签到,获得积分20
7秒前
Eric发布了新的文献求助30
7秒前
8秒前
欢呼的棒棒糖完成签到,获得积分10
8秒前
9秒前
10秒前
10秒前
10秒前
shaw完成签到,获得积分10
11秒前
11秒前
11秒前
桐桐应助Mannose采纳,获得10
12秒前
桓某人发布了新的文献求助10
12秒前
sq1997发布了新的文献求助10
12秒前
lee完成签到,获得积分20
12秒前
lmh发布了新的文献求助10
12秒前
高高高发布了新的文献求助10
13秒前
13秒前
15秒前
阿瓦隆的蓝胖子完成签到,获得积分10
15秒前
乐乐应助Linda采纳,获得10
15秒前
光亮小笼包完成签到 ,获得积分10
15秒前
北冥有鱼发布了新的文献求助10
16秒前
桓某人完成签到,获得积分10
16秒前
MeSs完成签到 ,获得积分10
16秒前
17秒前
英俊的铭应助77采纳,获得10
17秒前
17秒前
情怀应助沉静的迎荷采纳,获得10
19秒前
我是老大应助阿凉采纳,获得10
20秒前
丘比特应助顺心的书包采纳,获得10
21秒前
高分求助中
Picture Books with Same-sex Parented Families: Unintentional Censorship 700
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
Effective Learning and Mental Wellbeing 300
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3974712
求助须知:如何正确求助?哪些是违规求助? 3519159
关于积分的说明 11197254
捐赠科研通 3255257
什么是DOI,文献DOI怎么找? 1797724
邀请新用户注册赠送积分活动 877130
科研通“疑难数据库(出版商)”最低求助积分说明 806132