文献类型：期刊文献

英文题名：Multimodal Fusion Network with Information Bottleneck Mamba and Intervention Enhancement for Retinal Disease Diagnosis

作者：Liu, Qing[1]; Zhu, Hongqing[1]; Qian, Tianwei[2]; Jiang, Qingchao[1]; Xu, Xun[2]; Huang, Bingcang[3]

机构：[1] East China University of Science and Technology, School of Information Science and Engineering, Shanghai, 200237, China; [2] Shanghai Jiao Tong University, Department of Ophthalmology, Shanghai General Hospital, Shanghai, 200080, China; [3] Gongli Hospital of Shanghai Pudong New Area, Department of Radiology, Shanghai, 200135, China

年份：2026

外文期刊名：IEEE Journal of Biomedical and Health Informatics

收录：EI(收录号：20262721051493);Scopus(收录号：2-s2.0-105043709189)

语种：英文

外文关键词：Diseases - Feature extraction - Filtration - Information fusion - Ophthalmology

摘要：Multimodal fundus disease diagnosis holds significant clinical value. Color fundus photography (CFP) captures superficial retinal vasculature and lesion distribution, while optical coherence tomography (OCT) reveals the microstructure of deep retinal tissues, providing complementary pathological information. However, existing multimodal approaches still suffer from redundant non-diagnostic features, spurious correlations, and insufficient fusion granularity, hindering accurate identification of complex retinal pathologies. To address these challenges, we propose IMIE-Net, a multimodal fusion network based on the Mamba information bottleneck and intervention-enhanced mechanisms. First, a multimodal feature extraction filter uses Mamba networks with different scanning strategies to extract modality-specific features from CFP and OCT, followed by an information bottleneck that removes irrelevant features to retain disease-relevant features. Second, an interference feature intensifier employs an interference-aware dynamic recognition mechanism to strengthen relevant feature dimensions while suppressing spurious correlations, enhancing discriminative power. Finally, a cross-modal fusion dynamic decision classifier integrates multimodal features through explicit and implicit dual-path fusion and performs dynamic decision optimization to improve diagnostic robustness. Extensive experiments on multiple fundus datasets have shown that IMIE-Net can achieve accurate and reliable diagnosis of fundus diseases. ? 2013 IEEE.