Bi-branch bidirectional coupled interaction fusion network for multi-retinal diseases diagnosis

Color fundus photography (CFP) serves as an effective screening tool for retinal disease (RD). The advancement of artificial intelligence (AI) technologies has significantly improved the ability to identify RD based on CFP. However, most existing AI-assisted models are tailored specifically to a single RD and are developed using private and single-source datasets. This results in limited generalizability, posing challenges in achieving reliable recognition and classification of multiple retinal diseases (MRDs) in real-world clinical practice. To tackle these issues, we collect 16 public CFP datasets, covering 16 retinal conditions. The inherent diversity introduced by multi-source MRD datasets presents substantial challenges for feature extraction in MRD diagnostics, thereby constraining its clinical applicability. We propose the Bi-Branch bidirectional coupled interaction fusion network (BBCIF-Net) for MRD diagnosis. The concurrent dual-branch architecture in BBCIF-Net effectively integrates the local features from convolutional neural networks with the global representation provided by transformers. Consequently, it enhances the multi-fold feature extraction capabilities for MRD. The coupling interaction between the dual branches occurs across multiple feature spaces, facilitated by the bidirectional feature coupling unit. Additionally, we adopt redundant feature elimination strategies in both branch networks. This approach not only enables the model to better focus on combining key characteristics of the diseases but also compresses the model's parameters to a certain extent. The comprehensive experimental results demonstrate the effectiveness of the proposed method in MRD identification and two disease grading tasks. Our code is available at: https://github.com/SB-Chen/MRDs-BBCIF-Net.