Ammonia-Net: A Multi-task Joint Learning Model for Multi-class Segmentation and Classification in Tooth-marked Tongue Diagnosis
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Abstract (arXiv)
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%.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 22 of its 87 references that are in the map. First posted on arXiv 5 Oct 2023.
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22 of 87 references.
References · 22
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
- 2016Densely Connected Convolutional Networkscited by 6,691
- 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
- 2018UNet++: A Nested U-Net Architecture for Medical Image Segmentationcited by 1,518
- 2017Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semanticscited by 1,345
- 2018ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Designcited by 1,132
- 2020Designing Network Design Spacescited by 954
- 2017PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruningcited by 882
- 2017A Survey on Multi-Task Learningcited by 838
- 2018End-to-End Multi-Task Learning with Attentioncited by 533
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- 2016Cross-stitch Networks for Multi-task Learningcited by 466
- 2020U2-Net: Going Deeper with Nested U-Structure for Salient Object Detectioncited by 410
- 2019AdaShare: Learning What To Share For Efficient Deep Multi-Task Learningcited by 159
- 2016Fully Convolutional Instance-aware Semantic Segmentationcited by 122
- 2015Instance-aware Semantic Segmentation via Multi-task Network Cascadescited by 120
- 2016Deep Multi-task Representation Learning: A Tensor Factorisation Approachcited by 69
- 2019Multitask Classification and Segmentation for Cancer Diagnosis in Mammographycited by 9
- 2022RCMNet: A deep learning model assists CAR-T therapy for leukemiacited by 9
- 2022Mixed-UNet: Refined Class Activation Mapping for Weakly-Supervised Semantic Segmentation with Multi-scale Inferencecited by 7
- 2022Neuro-Symbolic Learning: Principles and Applications in Ophthalmologycited by 7
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