干旱区生态环境知识资源中心(Arid): MAD-Former: A Traceable Interpretability Model for Alzheimer's Disease Recognition Based on Multi-Patch Attention

Arid

DOI	10.1109/JBHI.2024.3368500
	MAD-Former: A Traceable Interpretability Model for Alzheimer's Disease Recognition Based on Multi-Patch Attention
	Ye, Jiayu; Zeng, An; Pan, Dan; Zhang, Yiqun; Zhao, Jingliang; Chen, Qiuping; Liu, Yang
通讯作者	Zeng, A
来源期刊	IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
ISSN	2168-2194
EISSN	2168-2208
出版年	2024
卷号	28 期号:6 页码:3637-3648
英文摘要	The integration of structural magnetic resonance imaging (sMRI) and deep learning techniques is one of the important research directions for the automatic diagnosis of Alzheimer's disease (AD). Despite the satisfactory performance achieved by existing voxel-based models based on convolutional neural networks (CNNs), such models only handle AD-related brain atrophy at a single spatial scale and lack spatial localization of abnormal brain regions based on model interpretability. To address the above limitations, we propose a traceable interpretability model for AD recognition based on multi-patch attention (MAD-Former). MAD-Former consists of two parts: recognition and interpretability. In the recognition part, we design a 3D brain feature extraction network to extract local features, followed by constructing a dual-branch attention structure with different patch sizes to achieve global feature extraction, forming a multi-scale spatial feature extraction framework. Meanwhile, we propose an important attention similarity position loss function to assist in model decision-making. The interpretability part proposes a traceable method that can obtain a 3D ROI space through attention-based selection and receptive field tracing. This space encompasses key brain tissues that influence model decisions. Experimental results reveal the significant role of brain tissues such as the Fusiform Gyrus (FuG) in AD recognition. MAD-Former achieves outstanding performance in different tasks on ADNI and OASIS datasets, demonstrating reliable model interpretability.
英文关键词	Alzheimer's disease medical image processing multi-patch features explainable algorithms
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001242344200030
WOS关键词	BASAL GANGLIA ; CORTEX ; TRANSFORMER ; DIAGNOSIS ; PATHOLOGY ; ROBUST
WOS类目	Computer Science, Information Systems ; Computer Science, Interdisciplinary Applications ; Mathematical & Computational Biology ; Medical Informatics
WOS研究方向	Computer Science ; Mathematical & Computational Biology ; Medical Informatics
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/404138
推荐引用方式 GB/T 7714	Ye, Jiayu,Zeng, An,Pan, Dan,et al. MAD-Former: A Traceable Interpretability Model for Alzheimer's Disease Recognition Based on Multi-Patch Attention[J],2024,28(6):3637-3648.
APA	Ye, Jiayu.,Zeng, An.,Pan, Dan.,Zhang, Yiqun.,Zhao, Jingliang.,...&Liu, Yang.(2024).MAD-Former: A Traceable Interpretability Model for Alzheimer's Disease Recognition Based on Multi-Patch Attention.IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS,28(6),3637-3648.
MLA	Ye, Jiayu,et al."MAD-Former: A Traceable Interpretability Model for Alzheimer's Disease Recognition Based on Multi-Patch Attention".IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 28.6(2024):3637-3648.