用大模型指导小模型，提升医疗图像分割的隐私保护训练效果
SAM-Fed: SAM-Guided Federated Semi-Supervised Learning for Medical Image Segmentation
- 大模型指导小模型，通过双知识蒸馏和自适应一致性机制优化伪标签
- 在皮肤病变和肠镜息肉分割上，性能优于现有联邦半监督方法
- 适合资源受限设备上的医疗图像分割，兼顾精度与部署可行性
医学图像分割具有重要临床价值，但数据隐私和专家标注成本限制了标注数据的获取。联邦半监督学习（FSSL）提供了解决方案，但仍面临两大挑战：伪标签可靠性依赖本地模型强度，且客户端设备因计算资源有限，常需使用轻量或异构架构。这导致伪标签质量与稳定性下降；而大模型虽更准确，却无法在客户端进行训练或常规推理。本文提出SAM-Fed，一种基于大容量分割基础模型引导轻量客户端的联邦半监督框架。SAM-Fed结合双重知识蒸馏与自适应一致性机制，实现像素级监督的精细化优化。在皮肤病变和肠镜息肉分割任务中，无论同质还是异构设置下，SAM-Fed均持续优于当前最优的FSSL方法。
原文摘要 · Abstract (English)
Medical image segmentation is clinically important, yet data privacy and the cost of expert annotation limit the availability of labeled data. Federated semi-supervised learning (FSSL) offers a solution but faces two challenges: pseudo-label reliability depends on the strength of local models, and client devices often require compact or heterogeneous architectures due to limited computational resources. These constraints reduce the quality and stability of pseudo-labels, while large models, though more accurate, cannot be trained or used for routine inference on client devices. We propose SAM-Fed, a federated semi-supervised framework that leverages a high-capacity segmentation foundation model to guide lightweight clients during training. SAM-Fed combines dual knowledge distillation with an adaptive agreement mechanism to refine pixel-level supervision. Experiments on skin lesion and polyp segmentation across homogeneous and heterogeneous settings show that SAM-Fed consistently outperforms state-of-the-art FSSL methods.
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