MPMNet: Modal Prior Mutual-Support Network for Age-Related Macular Degeneration Classification

doi:10.1007/978-3-031-72378-0_68

科研成果详情

题名	MPMNet: Modal Prior Mutual-Support Network for Age-Related Macular Degeneration Classification
作者	Li, Yuanyuan 1,2; Hao, Huaying 1; Zhang, Dan 3; Fu, Huazhu 4; Liu, Mengting 5; Shan, Caifeng 6; Zhao, Yitian 1; Zhang, Jiong 1
会议录名称	SPRINGER INTERNATIONAL PUBLISHING AG 影响因子和分区
语种	英语
原始文献类型	Proceedings Paper
会议名称	27th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)
会议日期	OCT 06-10, 2024
会议地点	Palmeraie Conf Ctr, Marrakesh, MOROCCO
关键词	Age-related macular degeneration CNV OCT OCTA Multi-modal
摘要	Early screening and classification of Age-related Macular Degeneration (AMD) are crucial for precise clinical treatment. Currently, most automated methods focus solely on dry and wet AMD classification. However, the classification of wet AMD into more explicit type 1 choroidal neovascularization (CNV) and type 2 CNV has rarely been explored, despite its significance in intravitreal injection. Furthermore, previous methods predominantly utilized single-modal images for distinguishing AMD types, while multi-modal images can provide a more comprehensive representation of pathological changes for accurate diagnosis. In this paper, we propose a Modal Prior Mutual-support Network (MPMNet), which for the first time combines OCTA images and OCT sequences for the classification of normal, dry AMD, type 1 CNV, and type 2 CNV. Specifically, we first employ a multi-branch encoder to extract modality-specific features. A novel modal prior mutual-support mechanism is proposed, which determines the primary and auxiliary modalities based on the sensitivity of different modalities to lesions and makes joint decisions. In this mechanism, a distillation loss is employed to enforce the consistency between single-modal decisions and joint decisions. It can facilitate networks to focus on specific pathological information within individual modalities. Furthermore, we propose a mutual information-guided feature dynamic adjustment strategy. This strategy adjusts the channel weights of the two modalities by computing the mutual information between OCTA and OCT, thereby mitigating the influence of low-quality modal features on the network's robustness. Experiments on private and public datasets have demonstrated that the proposed MPMNet outperforms existing state-of-the-art methods.
出版地	CHAM
EISSN	1611-3349
页码	733-742
DOI	10.1007/978-3-031-72378-0_68
页数	10
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods ; Engineering, Biomedical ; Radiology, Nuclear Medicine & Medical Imaging
WOS研究方向	Computer Science ; Engineering ; Radiology, Nuclear Medicine & Medical Imaging
WOS记录号	WOS:001342205800068
收录类别	CPCI-S
发表日期	2024
通讯作者地址	[Zhao, Yitian;Zhang, Jiong]Chinese Acad Sci, Ningbo Inst Mat Technol & Engn, Lab Adv Theranost Mat & Technol, Ningbo, Peoples R China.
引用统计
文献类型	会议论文
条目标识符	https://kms.wmu.edu.cn/handle/3ETUA0LF/223553
专题	其他_温州医科大学慈溪生物医药研究院
通讯作者	Zhao, Yitian; Zhang, Jiong
作者单位	1.Chinese Acad Sci, Ningbo Inst Mat Technol & Engn, Lab Adv Theranost Mat & Technol, Ningbo, Peoples R China; 2.Wenzhou Med Univ, Cixi Biomed Res Inst, Ningbo, Peoples R China; 3.Ningbo Univ Technol, Sch Cyber Sci & Engn, Ningbo, Peoples R China; 4.Agcy Sci Technol & Res, Inst High Performance Comp, Singapore 138632, Singapore; 5.Sun Yat Sen Univ, Sch Biomed Engn, Shenzhen, Peoples R China; 6.Nanjing Univ, Sch Biomed Engn, Nanjing, Peoples R China
第一作者单位	其他_温州医科大学慈溪生物医药研究院
推荐引用方式 GB/T 7714	Li, Yuanyuan,Hao, Huaying,Zhang, Dan,et al. MPMNet: Modal Prior Mutual-Support Network for Age-Related Macular Degeneration Classification[C]. CHAM,2024:733-742.