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Abstract：
Intelligent Traditional Chinese Medicine can provide people with a convenient way to participate in daily health care. The ease of acceptance of Traditional Chinese Medicine is also a major advantage in promoting health management. In Traditional Chinese Medicine, tongue imaging is an important step in the examination process. The segmentation and processing of the tongue image directly affects the results of intelligent Traditional Chinese Medicine diagnosis. As intelligent Traditional Chinese Medicine continues to develop, remote diagnosis and patient participation will play important roles. Smartphone sensor cameras can provide irreplaceable data collection capabilities in enhancing interaction in smart Traditional Chinese Medicine. However, these factors lead to differences in the size and quality of the captured images due to factors such as differences in shooting equipment, professionalism of the photographer, and the subject\'s cooperation. Most current tongue image segmentation algorithms are based on data collected by professional tongue diagnosis instruments in standard environments, and are not able to demonstrate the tongue image segmentation effect in complex environments. Therefore, we propose a segmentation algorithm for tongue images collected in complex multi-device and multi-user environments. We use convolutional attention and extend state space models to the 2D environment in the encoder. Then, cross-layer connection fusion is used in the decoder part to fuse shallow texture and deep semantic features. Through segmentation experiments on tongue image datasets collected by patients and doctors in real-world settings, our algorithm significantly improves segmentation performance and accuracy.
摘要：
智能中医可以为人们提供一种参与日常保健的便捷方式。中医容易接受也是促进健康管理的一大优势。在中医中,舌成像是检查过程中的重要步骤。舌象的分割和处理直接影响到智能中医诊断的结果。随着智能中医药的不断发展,远程诊断和患者参与将发挥重要作用。智能手机传感器摄像头可以提供不可替代的数据收集功能，以增强智能中医的交互性。然而,由于拍摄设备的差异等因素，这些因素导致拍摄图像的大小和质量的差异，摄影师的专业精神，和主体的合作。目前的舌象分割算法大多是基于专业舌诊仪器在标准环境下采集的数据,在复杂环境下无法演示舌象分割效果。因此,我们提出了一种在复杂的多设备和多用户环境中收集的舌头图像的分割算法。我们使用卷积注意力并将状态空间模型扩展到编码器中的2D环境。然后，解码器部分采用跨层连接融合来融合浅层纹理和深层语义特征。通过对患者和医生在真实世界环境中收集的舌头图像数据集的分割实验，我们的算法显著提高了分割性能和准确性。
