A Progressive Single-Modality to Multi-modality Classification Framework for Alzheimer’s Disease Sub-type Diagnosis

doi:10.1007/978-3-031-78761-4_12

	A Progressive Single-Modality to Multi-modality Classification Framework for Alzheimer’s Disease Sub-type Diagnosis
	Liu, Yuxiao1 ; Liu, Mianxin 2; Zhang, Yuanwang1 ; Sun, Kaicong1 ; Shen, Dinggang1,3,4
	2025
会议录名称	LECTURE NOTES IN COMPUTER SCIENCE (INCLUDING SUBSERIES LECTURE NOTES IN ARTIFICIAL INTELLIGENCE AND LECTURE NOTES IN BIOINFORMATICS)
ISSN	0302-9743
卷号	15266 LNCS
页码	123-133
DOI	10.1007/978-3-031-78761-4_12
摘要	The current clinical diagnosis framework of Alzheimer’s disease (AD) involves multiple modalities acquired from multiple diagnosis stages, each with distinct usage and cost. Previous AD diagnosis research has predominantly focused on how to directly fuse multiple modalities for an end-to-end one-stage diagnosis, which practically requires a high cost in data acquisition. Moreover, a significant part of these methods diagnose AD without considering clinical guideline and cannot offer accurate sub-type diagnosis. In this paper, by exploring inter-correlation among multiple modalities, we propose a novel progressive AD sub-type diagnosis framework, aiming to give diagnosis results based on easier-to-access modalities in earlier low-cost stages, instead of all modalities from all stages. Specifically, first, we design 1) a text disentanglement network for better processing tabular data collected in the initial stage, and 2) a modality fusion module for fusing multi-modality features separately. Second, we align features from modalities acquired in earlier low-cost stage(s) with later high-cost stage(s) to give accurate diagnosis without actual modality acquisition in later-stage(s) for saving cost. Furthermore, we follow the clinical guideline to align features at each stage for achieving sub-type diagnosis. Third, we leverage a progressive classifier that can progressively include additional acquired modalities (if needed) for diagnosis, to achieve the balance between diagnosis cost and diagnosis performance. We evaluate our proposed framework on large diverse public and in-home datasets (8280 subjects in total) and achieve superior performance over state-of-the-art methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
关键词	Adversarial machine learning Contrastive Learning Cost benefit analysis Data acquisition Diagnosis Large datasets Alzheimer Alzheimer’s disease Clinical guideline Explanation analyze High costs Multi-modality Multi-modality fusion Multi-stage framework Multi-stages Multiple modalities
会议名称	7th International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2024, Held in Conjunction with 27th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2024
会议地点	Marrakesh, Morocco
会议日期	October 10, 2024 - October 10, 2024
收录类别	EI
语种	英语
出版者	Springer Science and Business Media Deutschland GmbH
EI入藏号	20245217566523
EI主题词	Neurodegenerative diseases
EISSN	1611-3349
EI分类号	102.1 ; 102.1.2 ; 1101.2 ; 1106.2 ; 911.1 Cost Accounting ; 912.2 Management
原始文献类型	Conference article (CA)
文献类型	会议论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/467897
专题	生物医学工程学院_PI研究组_沈定刚组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_孙开聪组
通讯作者	Shen, Dinggang
作者单位	1.School of Biomedical Engineering and State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai; 201210, China; 2.Shanghai Artificial Intelligence Laboratory, Shanghai; 200232, China; 3.Shanghai United Imaging Intelligence Co., Ltd., Shanghai; 200230, China; 4.Shanghai Clinical Research and Trial Center, Shanghai; 201210, China
第一作者单位	上海科技大学
通讯作者单位	上海科技大学
第一作者的第一单位	上海科技大学
推荐引用方式 GB/T 7714	Liu, Yuxiao,Liu, Mianxin,Zhang, Yuanwang,et al. A Progressive Single-Modality to Multi-modality Classification Framework for Alzheimer’s Disease Sub-type Diagnosis[C]:Springer Science and Business Media Deutschland GmbH,2025:123-133.