基于符号导向的多标签舌诊框架，提升远程医疗中舌象识别精度
Dr. Tongue: Sign-Oriented Multi-label Detection for Remote Tongue Diagnosis
- 设计符号导向网络，模拟医生诊断流程识别舌象特征
- 构建面向远程诊疗的多标签舌象数据集，支持全面健康评估
- 可公开共享数据集，助力远程中医舌诊研究
舌诊是西医与中医的重要工具，通过分析舌部特征为患者健康提供关键信息。新冠疫情加剧了对精准远程医疗评估的需求，凸显了通过远程医疗实现舌象属性准确识别的重要性。为此，我们提出一种符号导向的多标签属性检测框架。该方法首先通过自适应舌象特征提取模块标准化图像并降低环境因素干扰，随后采用符号导向网络（SignNet）识别特定舌象属性，模拟资深医师的诊断过程，实现全面健康评估。为验证方法有效性，我们构建了一个专为远程诊疗设计的大型舌象图像数据集，其标签体系完整、覆盖广泛。该数据集将公开共享，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上均表现更优，展现出作为远程医疗评估核心工具的巨大潜力。
原文摘要 · Abstract (English)
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.
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