Dr. Tongue：面向远程舌诊的征象导向多标签检测
Dr. Tongue: Sign-Oriented Multi-label Detection for Remote Tongue Diagnosis
AI总结：
针对远程舌诊需求，本文提出征象导向多标签属性检测框架，结合自适应特征提取与SignNet，并构建开放的远程医疗舌象数据集，初步实验显示检测准确率提升。
摘要：
舌诊是西方医学和中医学中的重要工具，通过分析舌象属性为患者健康提供关键洞察。COVID-19疫情提升了对准确远程医疗评估的需求，凸显了通过远程医疗精确识别舌象属性的重要性。为解决这一问题，我们提出一种征象导向的多标签属性检测框架。该方法首先采用自适应舌部特征提取模块，对舌象图像进行标准化并缓解环境因素影响；随后通过征象导向网络（SignNet）识别特定舌象属性，模拟经验丰富医师的诊断过程，实现全面健康评估。为验证方法，我们构建了一个专为远程医疗设计的大规模舌象图像数据集。与现有数据集不同，该数据集面向远程诊断，并包含全面的属性标签。该数据集将公开发布，为研究提供宝贵资源。初步测试表明，该框架在多种舌象属性检测中提升了准确率，凸显其作为远程医疗评估重要工具的潜力。
英文摘要：
Tongue diagnosis is a vital tool in Western and Traditional Chinese Medicine, providing key insights into a patient's health by analyzing tongue attributes. The COVID-19 pandemic has heightened the need for accurate remote medical assessments, emphasizing the importance of precise tongue attribute recognition via telehealth. To address this, we propose a Sign-Oriented multi-label Attributes Detection framework. Our approach begins with an adaptive tongue feature extraction module that standardizes tongue images and mitigates environmental factors. This is followed by a Sign-oriented Network (SignNet) that identifies specific tongue attributes, emulating the diagnostic process of experienced practitioners and enabling comprehensive health evaluations. To validate our methodology, we developed an extensive tongue image dataset specifically designed for telemedicine. Unlike existing datasets, ours is tailored for remote diagnosis, with a comprehensive set of attribute labels. This dataset will be openly available, providing a valuable resource for research. Initial tests have shown improved accuracy in detecting various tongue attributes, highlighting our framework's potential as an essential tool for remote medical assessments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
