Work

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

Year: 2025

Type: article

Abstract: 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.

Source: Big Data Mining and Analytics

DOI: 10.26599/bdma.2025.9020020