标题： 用于医学图像分割的SAM高效知识蒸馏 显示英文标题

标题： Efficient Knowledge Distillation of SAM for Medical Image Segmentation

摘要： 《任意物体分割模型》（SAM）在交互式图像分割领域树立了新的标杆，在各种任务中表现出稳健的性能。然而，其较高的计算需求限制了它在实时或资源受限环境中的部署。 为了解决这些挑战，我们提出了一种新颖的知识蒸馏方法，即KD SAM，该方法通过均方误差（MSE）和感知损失的结合来优化编码器和解码器。此双损失框架捕捉结构和语义特征，使学生模型能够在降低计算复杂度的同时保持高分割精度。 基于在包括Kvasir-SEG、ISIC 2017、胎儿头部超声波和乳腺超声波在内的数据集上的模型评估，我们展示了KD SAM与基线模型相比具有可比或更优的性能，但参数显著减少。 KD SAM有效地平衡了分割准确性和计算效率，使其非常适合资源受限环境下的实时医学图像分割应用。 显示英文摘要

摘要： The Segment Anything Model (SAM) has set a new standard in interactive image segmentation, offering robust performance across various tasks. However, its significant computational requirements limit its deployment in real-time or resource-constrained environments. To address these challenges, we propose a novel knowledge distillation approach, KD SAM, which incorporates both encoder and decoder optimization through a combination of Mean Squared Error (MSE) and Perceptual Loss. This dual-loss framework captures structural and semantic features, enabling the student model to maintain high segmentation accuracy while reducing computational complexity. Based on the model evaluation on datasets, including Kvasir-SEG, ISIC 2017, Fetal Head Ultrasound, and Breast Ultrasound, we demonstrate that KD SAM achieves comparable or superior performance to the baseline models, with significantly fewer parameters. KD SAM effectively balances segmentation accuracy and computational efficiency, making it well-suited for real-time medical image segmentation applications in resource-constrained environments.