轻量级适配器让SAM模型高效迁移至医学影像分割
SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation
- 将适配器置于掩码解码器，仅训练不足1%参数
- 在四个数据集上性能接近全量微调，且通用性更强
- 适合资源有限但需快速部署医学图像分割的场景
本文针对医学影像语义分割中的域适应问题。尽管近期基础分割模型如SAM在自然图像上表现优异，但在医学图像上效果不佳。此外，现有端到端微调方法计算成本过高。为此，我们提出一种新型SAM适配器方法，在最小化可训练参数的同时达到与全量微调相当的性能。该适配器被战略性地放置于掩码解码器中，展现出出色的泛化能力，并在完全监督和测试时域适应任务中均提升了分割效果。在四个数据集上的广泛验证表明，该方法优于现有技术，且训练参数少于SAM总参数的1%。
原文摘要 · Abstract (English)
This paper addresses the domain adaptation challenge for semantic segmentation in medical imaging. Despite the impressive performance of recent foundational segmentation models like SAM on natural images, they struggle with medical domain images. Beyond this, recent approaches that perform end-to-end fine-tuning of models are simply not computationally tractable. To address this, we propose a novel SAM adapter approach that minimizes the number of trainable parameters while achieving comparable performances to full fine-tuning. The proposed SAM adapter is strategically placed in the mask decoder, offering excellent and broad generalization capabilities and improved segmentation across both fully supervised and test-time domain adaptation tasks. Extensive validation on four datasets showcases the adapter's efficacy, outperforming existing methods while training less than 1% of SAM's total parameters.
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