A Graph-Embedded Latent Space Learning and Clustering Framework for Incomplete Multimodal Multiclass Alzheimer's Disease Diagnosis

doi:10.1007/978-3-031-72104-5_5

	A Graph-Embedded Latent Space Learning and Clustering Framework for Incomplete Multimodal Multiclass Alzheimer's Disease Diagnosis
	Ou, Zaixin1,2 ; Jiang, Caiwen1,2 ; Liu, Yuxiao1,2 ; Zhang, Yuanwang1,2 ; Cui, Zhiming1,2 ; Shen, Dinggang1,2,3,4
	2024
会议录名称	MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2024, PT VII (IF:0.402[JCR-2005],0.000[5-Year])
ISSN	0302-9743
卷号	15007
发表状态	已发表
DOI	10.1007/978-3-031-72104-5_5
摘要	Alzheimer's disease (AD) is an irreversible neurodegenerative disease, where early diagnosis is crucial for improving prognosis and delaying the progression of the disease. Leveraging multimodal PET images, which can reflect various biomarkers like A beta and tau protein, is a promising method for AD diagnosis. However, due to the high cost and practical issues of PET imaging, it often faces challenges with incomplete multimodal data. To address this dilemma, in this paper, we propose a Graph-embedded latent Space Learning and Clustering framework, named Graph-SLC, for multiclass AD diagnosis under incomplete multimodal data scenarios. The key concept is leveraging all available subjects, including those with incomplete modality data, to train a network for projecting subjects into their latent representations. These latent representations not only exploit the complementarity of different modalities but also showcase separability among different classes. Specifically, our Graph-SLC consists of three modules, i.e., a multimodal reconstruction module, a subject-similarity graph embedding module, and an AD-oriented latent clustering module. Among them, the multimodal reconstruction module generates subject-specific latent representations that can comprehensively incorporate information from different modalities with guidance from all available modalities. The subject-similarity graph embedding module then enhances the discriminability of different latent representations by ensuring the neighborhood relationships between subjects are preserved in subject-specific latent representations. The ADoriented latent clustering module facilitates the separability of multiple classes by constraining subject-specific latent representations within the same class to be in the same cluster. Experiments on the ADNI show that our method achieves state-of-the-art performance in multiclass AD diagnosis. Our code is available at https://github.com/Ouzaixin/GraphSLC.
关键词	Alzheimer's Disease Incomplete Multimodal Learning Graph Embedding Latent Space Clustering
会议名称	27th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)
出版地	GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
会议地点	Palmeraie Conf Ctr,Marrakesh,MOROCCO
会议日期	OCT 06-10, 2024
URL	查看原文
收录类别	CPCI-S
语种	英语
资助项目	National Natural Science Foundation of China["62131015","62250710165","U23A20295","2022ZD0209000"] ; Shanghai Municipal Central Guided Local Science and Technology Development Fund[YDZX20233100001001] ; Science and Technology Commission of Shanghai Municipality (STCSM)[21010502600] ; Key R&D Program of Guangdong Province, China["2023B0303040001","2021B0101420006"]
WOS研究方向	Computer Science ; Radiology, Nuclear Medicine & Medical Imaging
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods ; Radiology, Nuclear Medicine & Medical Imaging
WOS记录号	WOS:001342232700005
出版者	SPRINGER INTERNATIONAL PUBLISHING AG
EISSN	1611-3349
文献类型	会议论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/452391
专题	生物医学工程学院信息科学与技术学院_硕士生信息科学与技术学院_博士生生物医学工程学院_PI研究组_沈定刚组生物医学工程学院_PI研究组_崔智铭组
通讯作者	Shen, Dinggang
作者单位	1.ShanghaiTech Univ, Sch Biomed Engn, Shanghai 201210, Peoples R China 2.ShanghaiTech Univ, State Key Lab Adv Med Mat & Devices, Shanghai 201210, Peoples R China 3.Shanghai United Imaging Intelligence Co Ltd, Shanghai 200232, Peoples R China 4.Shanghai Clin Res & Trial Ctr, Shanghai 201210, Peoples R China
第一作者单位	生物医学工程学院; 上海科技大学
通讯作者单位	生物医学工程学院; 上海科技大学
第一作者的第一单位	生物医学工程学院
推荐引用方式 GB/T 7714	Ou, Zaixin,Jiang, Caiwen,Liu, Yuxiao,et al. A Graph-Embedded Latent Space Learning and Clustering Framework for Incomplete Multimodal Multiclass Alzheimer's Disease Diagnosis[C]. GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND:SPRINGER INTERNATIONAL PUBLISHING AG,2024.