Cargando…

Weakly Supervised Deep Learning for Tooth-Marked Tongue Recognition

The recognition of tooth-marked tongues has important value for clinical diagnosis of traditional Chinese medicine. Tooth-marked tongue is often related to spleen deficiency, cold dampness, sputum, effusion, and blood stasis. The clinical manifestations of patients with tooth-marked tongue include l...

Descripción completa

Detalles Bibliográficos
Autores principales: Zhou, Jianguo, Li, Shangxuan, Wang, Xuesong, Yang, Zizhu, Hou, Xinyuan, Lai, Wei, Zhao, Shifeng, Deng, Qingqiong, Zhou, Wu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9039050/
https://www.ncbi.nlm.nih.gov/pubmed/35492602
http://dx.doi.org/10.3389/fphys.2022.847267
_version_ 1784694037915107328
author Zhou, Jianguo
Li, Shangxuan
Wang, Xuesong
Yang, Zizhu
Hou, Xinyuan
Lai, Wei
Zhao, Shifeng
Deng, Qingqiong
Zhou, Wu
author_facet Zhou, Jianguo
Li, Shangxuan
Wang, Xuesong
Yang, Zizhu
Hou, Xinyuan
Lai, Wei
Zhao, Shifeng
Deng, Qingqiong
Zhou, Wu
author_sort Zhou, Jianguo
collection PubMed
description The recognition of tooth-marked tongues has important value for clinical diagnosis of traditional Chinese medicine. Tooth-marked tongue is often related to spleen deficiency, cold dampness, sputum, effusion, and blood stasis. The clinical manifestations of patients with tooth-marked tongue include loss of appetite, borborygmus, gastric distention, and loose stool. Traditional clinical tooth-marked tongue recognition is conducted subjectively based on the doctor’s visual observation, and its performance is affected by the doctor’s subjectivity, experience, and environmental lighting changes. In addition, the tooth marks typically have various shapes and colors on the tongue, which make it very challenging for doctors to identify tooth marks. The existing methods based on deep learning have made great progress for tooth-marked tongue recognition, but there are still shortcomings such as requiring a large amount of manual labeling of tooth marks, inability to detect and locate the tooth marks, and not conducive to clinical diagnosis and interpretation. In this study, we propose an end-to-end deep neural network for tooth-marked tongue recognition based on weakly supervised learning. Note that the deep neural network only requires image-level annotations of tooth-marked or non-tooth marked tongues. In this method, a deep neural network is trained to classify tooth-marked tongues with the image-level annotations. Then, a weakly supervised tooth-mark detection network (WSTDN) as an architecture variant of the pre-trained deep neural network is proposed for the tooth-marked region detection. Finally, the WSTDN is re-trained and fine-tuned using only the image-level annotations to simultaneously realize the classification of the tooth-marked tongue and the positioning of the tooth-marked region. Experimental results of clinical tongue images demonstrate the superiority of the proposed method compared with previously reported deep learning methods for tooth-marked tongue recognition. The proposed tooth-marked tongue recognition model may provide important syndrome diagnosis and efficacy evaluation methods, and contribute to the understanding of ethnopharmacological mechanisms.
format Online
Article
Text
id pubmed-9039050
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-90390502022-04-27 Weakly Supervised Deep Learning for Tooth-Marked Tongue Recognition Zhou, Jianguo Li, Shangxuan Wang, Xuesong Yang, Zizhu Hou, Xinyuan Lai, Wei Zhao, Shifeng Deng, Qingqiong Zhou, Wu Front Physiol Physiology The recognition of tooth-marked tongues has important value for clinical diagnosis of traditional Chinese medicine. Tooth-marked tongue is often related to spleen deficiency, cold dampness, sputum, effusion, and blood stasis. The clinical manifestations of patients with tooth-marked tongue include loss of appetite, borborygmus, gastric distention, and loose stool. Traditional clinical tooth-marked tongue recognition is conducted subjectively based on the doctor’s visual observation, and its performance is affected by the doctor’s subjectivity, experience, and environmental lighting changes. In addition, the tooth marks typically have various shapes and colors on the tongue, which make it very challenging for doctors to identify tooth marks. The existing methods based on deep learning have made great progress for tooth-marked tongue recognition, but there are still shortcomings such as requiring a large amount of manual labeling of tooth marks, inability to detect and locate the tooth marks, and not conducive to clinical diagnosis and interpretation. In this study, we propose an end-to-end deep neural network for tooth-marked tongue recognition based on weakly supervised learning. Note that the deep neural network only requires image-level annotations of tooth-marked or non-tooth marked tongues. In this method, a deep neural network is trained to classify tooth-marked tongues with the image-level annotations. Then, a weakly supervised tooth-mark detection network (WSTDN) as an architecture variant of the pre-trained deep neural network is proposed for the tooth-marked region detection. Finally, the WSTDN is re-trained and fine-tuned using only the image-level annotations to simultaneously realize the classification of the tooth-marked tongue and the positioning of the tooth-marked region. Experimental results of clinical tongue images demonstrate the superiority of the proposed method compared with previously reported deep learning methods for tooth-marked tongue recognition. The proposed tooth-marked tongue recognition model may provide important syndrome diagnosis and efficacy evaluation methods, and contribute to the understanding of ethnopharmacological mechanisms. Frontiers Media S.A. 2022-04-12 /pmc/articles/PMC9039050/ /pubmed/35492602 http://dx.doi.org/10.3389/fphys.2022.847267 Text en Copyright © 2022 Zhou, Li, Wang, Yang, Hou, Lai, Zhao, Deng and Zhou. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Physiology
Zhou, Jianguo
Li, Shangxuan
Wang, Xuesong
Yang, Zizhu
Hou, Xinyuan
Lai, Wei
Zhao, Shifeng
Deng, Qingqiong
Zhou, Wu
Weakly Supervised Deep Learning for Tooth-Marked Tongue Recognition
title Weakly Supervised Deep Learning for Tooth-Marked Tongue Recognition
title_full Weakly Supervised Deep Learning for Tooth-Marked Tongue Recognition
title_fullStr Weakly Supervised Deep Learning for Tooth-Marked Tongue Recognition
title_full_unstemmed Weakly Supervised Deep Learning for Tooth-Marked Tongue Recognition
title_short Weakly Supervised Deep Learning for Tooth-Marked Tongue Recognition
title_sort weakly supervised deep learning for tooth-marked tongue recognition
topic Physiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9039050/
https://www.ncbi.nlm.nih.gov/pubmed/35492602
http://dx.doi.org/10.3389/fphys.2022.847267
work_keys_str_mv AT zhoujianguo weaklysuperviseddeeplearningfortoothmarkedtonguerecognition
AT lishangxuan weaklysuperviseddeeplearningfortoothmarkedtonguerecognition
AT wangxuesong weaklysuperviseddeeplearningfortoothmarkedtonguerecognition
AT yangzizhu weaklysuperviseddeeplearningfortoothmarkedtonguerecognition
AT houxinyuan weaklysuperviseddeeplearningfortoothmarkedtonguerecognition
AT laiwei weaklysuperviseddeeplearningfortoothmarkedtonguerecognition
AT zhaoshifeng weaklysuperviseddeeplearningfortoothmarkedtonguerecognition
AT dengqingqiong weaklysuperviseddeeplearningfortoothmarkedtonguerecognition
AT zhouwu weaklysuperviseddeeplearningfortoothmarkedtonguerecognition