文献类型：期刊文献

英文题名：Adaptive Evidential Fusion of Light–Dark Features With Multi-Scan Mamba for Automated Macular Edema Diagnosis

作者：Zhang, Yiming[1]; Zhu, Hongqing[1]; Qian, Tianwei[2]; Hou, Tong[1]; Chen, Ning[1]; Xu, Xun[2]; Huang, Bingcang[3]

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

年份：2026

卷号：36

期号：1

外文期刊名：International Journal of Imaging Systems and Technology

收录：EI(收录号：20255019705877)

语种：英文

外文关键词：Automation - Color photography - Decision making - Decision theory - Deep learning - Diagnosis - Information fusion - Learning systems - Medical imaging - Ophthalmology

摘要：Macular edema is a retinal disorder that can lead to significant vision loss, underscoring the need for accurate and intelligent automated diagnosis. However, its subtle manifestations in color fundus photography (CFP) pose considerable challenges for conventional deep learning models. In this work, we propose a novel diagnostic framework that integrates Dempster–Shafer (D–S) evidence theory—a principled approach for uncertainty quantification and multi-source information fusion—with the advanced Mamba architecture. The proposed method employs a dual-branch network to selectively enhance and extract discriminative features from both bright and dark regions of fundus images. These features are dynamically aligned and fused via an Adaptive Multi-Branch Feature Synthesis (AMFS) module. To model long-range dependencies and aggregate complementary information from multiple scanning views, we introduce a multi-scan Mamba module, whose outputs are further fused using a principled D–S evidence mechanism. This synergistic integration of mathematical theory and deep learning not only reduces information redundancy but also enables confidence-aware automated decision-making. Extensive experiments on three retinal image datasets—IDRiD, Messidor, and a proprietary clinical cohort—demonstrate that our framework consistently outperforms state-of-the-art methods in terms of accuracy, F1-score, and robustness. These results highlight the promise of combining evidence theory with modern deep learning for challenging medical image analysis tasks. ? 2025 Wiley Periodicals LLC.