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arXiv 2310.03472cs.CV

Ammonia-Net：一种用于齿痕舌诊断中多类分割与分类的多任务联合学习模型

Ammonia-Net: A Multi-task Joint Learning Model for Multi-class Segmentation and Classification in Tooth-marked Tongue Diagnosis

School of Mechanical, Electrical & Information Engineering, Shandong University（山东大学机电与信息工程学院）；SDU-ANU Joint Science College, Shandong University（山东大学山东大学-澳大利亚国立大学联合科学学院）；Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院生物医药与健康工程研究院）；School of Software Engineering, Electronic and Information Engineering, Xi'an Jiaotong University（西安交通大学软件与电子信息工程学院）；Department of Radiology, Shenzhen Children's Hospital（深圳市儿童医院放射科）

Shunkai Shi, Yuqi Wang, Qihui Ye, Yanran Wang, Yiming Zhu, Muhammad Hassan, Aikaterini Melliou, Dongmei Yu

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AI总结：

针对中医齿痕舌人工诊断准确性与一致性不足的问题，提出多任务联合学习模型Ammonia-Net，结合语义分割与分类实现齿痕舌分级诊断，在自建数据集上取得良好效果。

摘要：

在中医领域，舌上的齿痕由长期牙齿压迫所致，是判断气（阳）虚的关键指标，而气虚与脏腑健康存在内在关联。人工诊断齿痕舌完全依赖经验，然而齿痕在形状、颜色和类型上的多样性，给诊断的准确性和一致性带来了挑战。为解决这些问题，本文提出一种名为Ammonia-Net的多任务联合学习模型。该模型采用基于卷积神经网络的架构，专门设计用于舌象图像的多类分割与分类。Ammonia-Net对舌象图像进行语义分割，以识别舌体和齿痕；在分割输出的辅助下，它将图像分为所需的类别：健康舌、轻度齿痕舌、中度齿痕舌和重度齿痕舌。据我们所知，这是首次将齿痕语义分割结果应用于齿痕舌分类的尝试。为训练Ammonia-Net，我们从856名受试者处收集了856张舌象图像。经过大量实验，结果表明，所提模型在齿痕舌识别的二分类任务中准确率达99.06%，（多分类任务准确率为）80.02%；在分割任务中，舌体和齿痕的平均交并比（mIoU）达71.65%。

英文摘要：

In Traditional Chinese Medicine, the tooth marks on the tongue, stemming from prolonged dental pressure, serve as a crucial indicator for assessing qi (yang) deficiency, which is intrinsically linked to visceral health. Manual diagnosis of tooth-marked tongue solely relies on experience. Nonetheless, the diversity in shape, color, and type of tooth marks poses a challenge to diagnostic accuracy and consistency. To address these problems, herein we propose a multi-task joint learning model named Ammonia-Net. This model employs a convolutional neural network-based architecture, specifically designed for multi-class segmentation and classification of tongue images. Ammonia-Net performs semantic segmentation of tongue images to identify tongue and tooth marks. With the assistance of segmentation output, it classifies the images into the desired number of classes: healthy tongue, light tongue, moderate tongue, and severe tongue. As far as we know, this is the first attempt to apply the semantic segmentation results of tooth marks for tooth-marked tongue classification. To train Ammonia-Net, we collect 856 tongue images from 856 subjects. After a number of extensive experiments, the experimental results show that the proposed model achieves 99.06% accuracy in the two-class classification task of tooth-marked tongue identification and 80.02%. As for the segmentation task, mIoU for tongue and tooth marks amounts to 71.65%.

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。

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