<p>Age-related macular degeneration (AMD) is a leading cause of visual impairment in middle-aged and elderly populations. Existing diagnostic methods are often inefficient and subjective. To achieve accurate and efficient diagnosis of AMD lesions, this paper designs BELD-Net, an efficient segmentation network based on a large visual model. The model employs DINOv2 as its backbone to capture long-range and cross-scale features of AMD lesions through large-scale self-supervised pre-training. Combined with the query-based segmentation mechanism of Mask2Former, it significantly improves generalization in few-shot scenarios. BELD-Net incorporates LoRA modules into the Transformer layers of DINOv2 for low-rank adaptation, reducing computational cost and enhancing the model’s adaptability to fundus images while avoiding overfitting. Additionally, a Multi-scale Boundary Enhancement Module (MBEM) is designed using heterogeneous convolutions and an adaptive squeeze-and-excitation (SE) mechanism to address blurred and variable-sized lesion boundaries, thereby improving sensitivity to low-contrast edges. Experimental results demonstrate that BELD-Net achieves outstanding performance in automatic multi-lesion segmentation of AMD, with an mDice value of 59.61%, outperforming existing methods. It also effectively reduces computational complexity, proving its potential for intelligent assisted diagnosis.</p>