Ammonia-Net: A Multi-task Joint Learning Model for Multi-class
  Segmentation and Classification in Tooth-marked Tongue Diagnosis
Shunkai Shi Yuqi Wang Qihui Ye Yanran Wang Yiming Zhu Muhammad Hassan Aikaterini Melliou Dongmei Yu
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%.