无需提示词的高效医学图像分割方法，精度提升近20%且推理快87%
Prompt-Free and Efficient SAM2 Adaptation for Biomedical Semantic Segmentation via Dual Adapters
- 用双适配器架构实现免提示微调，支持任意尺寸输入
- 在多个医学数据集上比原版SAM2准确率最高提升19.66%
- 轻量适配器使计算成本降低约87%，适合临床部署
Segment Anything Model 2 (SAM2) 在自然图像上表现出色，但在医学图像分割中因领域差异大和依赖提示而受限。为此，我们提出一种免提示、参数高效的微调框架，适用于多类别、可变尺寸输入的医学语义分割。引入卷积位置编码生成器以适应任意长宽比，并设计双适配器策略：高精度适配器采用可变形卷积精确建模边界，轻量适配器通过结构重参数化最小化推理延迟。在ISBI 2012、Kvasir-SEG、Synapse和ACDC数据集上的实验表明，该方法显著优于强基线。相比原版SAM2，分割精度最高提升19.66%；与重型医学适配方法相比，计算成本降低约87%，实现了精度与效率的更优权衡。
原文摘要 · Abstract (English)
Segment Anything Model 2 (SAM2) demonstrated impressive zero-shot capabilities on natural images but faces challenges in biomedical segmentation due to significant domain shifts and prompt dependency. To address these limitations, we propose a prompt-free, parameter-efficient fine-tuning framework designed for multi-class segmentation on variable-sized inputs. We introduce a convolutional Positional Encoding Generator to adapt effectively to arbitrary aspect ratios and present a dual-adapter strategy: High-Performance Adapter utilizing deformable convolutions for precise boundary modeling and Lightweight Adapter employing structural re-parameterization to minimize inference latency. Experiments on ISBI 2012, Kvasir-SEG, Synapse, and ACDC datasets demonstrate that our approach significantly outperforms strong adaptation baselines. Specifically, our method improved segmentation accuracy by up to 19.66\% over the vanilla SAM2, while reducing computational costs by approximately 87\% compared to heavyweight medical SAM adaptations, establishing a superior trade-off between accuracy and efficiency.
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