Weakly supervised object detection for automatic tooth-marked tongue recognition

Yongcun Zhang

doi:10.1016/j.bspc.2025.107766

Weakly supervised object detection for automatic tooth-marked tongue recognition

2025-03-01

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AI

DOI：10.1016/j.bspc.2025.107766

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Tongue diagnosis in Traditional Chinese Medicine (TCM) is a crucial diagnostic method that can an individual's health status. Traditional methods for identifying tooth-marked tongues are subjective inconsistent because they rely on practitioner experience. We propose a novel fully automated W Supervised method using Vision transformer and Multiple instance learning (WSVM) for tongue extraction and tooth-marked tongue recognition. Our approach first accurately detects and extracts the tongue from clinical images, removing any irrelevant background information. Then, we implement an end-to weakly supervised object detection method. We utilize Vision Transformer (ViT) to process tongue images patches and employ multiple instance loss to identify tooth-marked regions with only image-level annotations. WSVM achieves high accuracy in tooth-marked tongue classification and tooth-marked tongue detection. Visualization experiments further demonstrate its effectiveness in pinpointing these regions. This automated approach enhances the objectivity and accuracy of tooth-marked tongue diagnosis. It provides significant clinical value by assisting TCM practitioners in making precise diagnoses and treatment recommendations. Code is available at https://github.com/yc-zh/WSVM.

Keyword:

Tooth-marked tongue

Weakly supervised

Tongue recognition

Weakly supervised object detection for automatic tooth-marked tongue recognition

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