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Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

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Weakly Supervised Deep Learning for Tooth-Marked Tongue Recognition.

Jianguo Zhou¹, Shangxuan Li¹, Xuesong Wang²

¹School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, China.

Frontiers in Physiology

|May 2, 2022

Summary

This summary is machine-generated.

This study introduces a weakly supervised deep learning model for recognizing tooth-marked tongues, improving traditional Chinese medicine diagnosis. The method accurately classifies and locates tooth marks using only image-level annotations.

Keywords:

convolutional neural network deep learning tongue diagnosis tooth-marked tongue traditional Chinese medicine weakly supervised learning

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Area of Science:

Traditional Chinese Medicine
Medical Imaging
Artificial Intelligence

Background:

Tooth-marked tongue diagnosis is crucial in Traditional Chinese Medicine (TCM), often indicating spleen deficiency, dampness, or blood stasis.
Current subjective visual assessment by TCM practitioners is limited by experience, lighting, and mark variability.
Existing deep learning methods require extensive manual labeling and cannot precisely locate tooth marks.

Purpose of the Study:

To develop an end-to-end deep neural network for tooth-marked tongue recognition using weakly supervised learning.
To enable accurate classification and localization of tooth marks with minimal annotation effort.
To overcome limitations of current subjective and automated diagnostic methods.

Main Methods:

Proposed an end-to-end deep neural network trained with image-level annotations (tooth-marked or not).
Developed a weakly supervised tooth-mark detection network (WSTDN) as a variant of the trained network.
Re-trained and fine-tuned WSTDN using only image-level data for simultaneous classification and localization.

Main Results:

The proposed weakly supervised deep learning method achieved superior performance in tooth-marked tongue recognition compared to existing deep learning approaches.
Experimental results on clinical tongue images validated the model's effectiveness.
The model successfully performs both classification and localization of tooth marks.

Conclusions:

The developed weakly supervised model offers an efficient and accurate tool for tooth-marked tongue recognition.
This approach supports TCM syndrome diagnosis, efficacy evaluation, and ethnopharmacological research.
It provides a more objective and interpretable method for clinical diagnosis.