用低参数量适配器高效微调SAM2，实现眼底图像精准分割。
SAM2LoRA: Composite Loss-Guided, Parameter-Efficient Finetuning of SAM2 for Retinal Fundus Segmentation
- 在图像编码器和掩码解码器中加入低秩适配器，仅需原参数5%以下。
- 跨数据集训练下，血管与视盘分割Dice达0.86和0.93，AUC超0.98。
- 复合损失函数提升泛化性，适合资源受限场景的医疗图像分割。
我们提出SAM2LoRA，一种参数高效的微调策略，用于将分割一切模型2（SAM2）适配至眼底图像分割任务。SAM2采用基于掩码自编码器预训练的分层视觉变压器进行多尺度特征解码，可在低资源环境下实现快速推理；然而微调仍具挑战。为此，SAM2LoRA在图像编码器和掩码解码器中集成低秩适配器，所需可训练参数少于原模型的5%。分析表明，在跨数据集眼底图像分割任务中，结合分割BCE、SoftDice与FocalTversky损失的复合损失函数对网络优化至关重要。在11个具有挑战性的眼底分割数据集上评估，SAM2LoRA在跨数据集训练条件下表现出色，血管与视盘分割的Dice分数分别达到0.86和0.93，AUC值高达0.98和0.99，性能达当前最优水平，同时显著降低训练开销。
原文摘要 · Abstract (English)
We propose SAM2LoRA, a parameter-efficient fine-tuning strategy that adapts the Segment Anything Model 2 (SAM2) for fundus image segmentation. SAM2 employs a masked autoencoder-pretrained Hierarchical Vision Transformer for multi-scale feature decoding, enabling rapid inference in low-resource settings; however, fine-tuning remains challenging. To address this, SAM2LoRA integrates a low-rank adapter into both the image encoder and mask decoder, requiring fewer than 5\% of the original trainable parameters. Our analysis indicates that for cross-dataset fundus segmentation tasks, a composite loss function combining segmentationBCE, SoftDice, and FocalTversky losses is essential for optimal network tuning. Evaluated on 11 challenging fundus segmentation datasets, SAM2LoRA demonstrates high performance in both blood vessel and optic disc segmentation under cross-dataset training conditions. It achieves Dice scores of up to 0.86 and 0.93 for blood vessel and optic disc segmentation, respectively, and AUC values of up to 0.98 and 0.99, achieving state-of-the-art performance while substantially reducing training overhead.
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