Retinal Fusion Network with Contrastive Learning for Imbalanced Multi-Class Retinal Disease Recognition in FFA

Precise multi-class retinal disease recognition faces challenges from inter/intra-class variations and imbalanced distributions. While Convolution Neural Network (CNNs) effectively capture salient lesions, they struggle with subtle lesions and exhibit bias toward frequent diseases. We propose a Retinal Lesion Fusion Network (RLF-Net) with two novel modules: a Retinal Lesion Feature Fusion (RLFF) module combining a SAlient Lesion Enhancement (SALE) block, SUbtle Lesion Enhancement (SULE) block, and Fast Fourier Transform Fusion (FFTF) block to adaptively integrate multi-scale lesion features and a Retinal Screening of Diseases (RSD) module mitigating class imbalance by equally weighting disease-specific feature differences. Additionally, we design a hybrid loss merging supervised contrastive learning and cross-entropy to enhance discriminative power. Evaluations on a clinical Fundus Fluorescein Angiography (FFA) dataset and two public fundus benchmarks demonstrate RLF-Net's superiority over state-of-the-art methods. Our approach advances multi-class retinal diagnosis by addressing critical limitations in feature representation and class imbalance, particularly improving recognition of subtle lesions and rare diseases through synergistic feature fusion and balanced optimization strategies.