标题： TOM：一种具有多教师蒸馏和任务特定数据增强的开源舌头分割方法 显示英文标题

标题： TOM: An Open-Source Tongue Segmentation Method with Multi-Teacher Distillation and Task-Specific Data Augmentation

摘要： 舌部成像作为一种有价值的诊断工具，特别是在中医（TCM）中。舌面分割的质量显著影响智能舌诊系统中舌部图像分类和后续诊断的准确性。然而，现有的舌部图像分割研究存在明显的局限性，缺乏稳健且用户友好的分割工具。本文提出了一种基于多教师知识蒸馏的舌部图像分割模型（TOM）。通过引入一种基于扩散的数据增强方法，我们在减少模型参数量的同时增强了分割模型的泛化能力。值得注意的是，在参数数量相比教师模型减少96.6%的情况下，学生模型仍实现了95.22% mIoU的出色分割性能。此外，我们将训练好的模型打包并部署为在线和离线分割工具（可在https://itongue.cn/获取），使中医从业者和研究人员无需任何编程经验即可使用。我们还展示了一个使用分割后的舌部区域进行中医体质分类的案例研究。实验结果表明，使用舌部区域进行训练比使用原始舌部图像具有更高的分类性能和更好的可解释性。据我们所知，这是第一个开源且免费的舌部图像分割工具。 显示英文摘要

摘要： Tongue imaging serves as a valuable diagnostic tool, particularly in Traditional Chinese Medicine (TCM). The quality of tongue surface segmentation significantly affects the accuracy of tongue image classification and subsequent diagnosis in intelligent tongue diagnosis systems. However, existing research on tongue image segmentation faces notable limitations, and there is a lack of robust and user-friendly segmentation tools. This paper proposes a tongue image segmentation model (TOM) based on multi-teacher knowledge distillation. By incorporating a novel diffusion-based data augmentation method, we enhanced the generalization ability of the segmentation model while reducing its parameter size. Notably, after reducing the parameter count by 96.6% compared to the teacher models, the student model still achieves an impressive segmentation performance of 95.22% mIoU. Furthermore, we packaged and deployed the trained model as both an online and offline segmentation tool (available at https://itongue.cn/), allowing TCM practitioners and researchers to use it without any programming experience. We also present a case study on TCM constitution classification using segmented tongue patches. Experimental results demonstrate that training with tongue patches yields higher classification performance and better interpretability than original tongue images. To our knowledge, this is the first open-source and freely available tongue image segmentation tool.