# Weakly Supervised Object Detection for Automatic Tooth-marked Tongue Recognition

Source: [Weakly Supervised Object Detection for Automatic Tooth-marked Tongue Recognition](<https://www.sota2.com/research/paper/68fa749f1e209131cea62b56>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T03:13:35.838Z

Authors: Yongcun Zhang, Jiajun Xu, Yina He, Shaozi Li, Zhiming Luo, Huangwei Lei

Published: 2024-08-29T11:31:28.000Z

## Abstract

Tongue diagnosis in Traditional Chinese Medicine (TCM) is a crucial diagnostic method that can reflect an individual's health status. Traditional methods for identifying tooth-marked tongues are subjective and inconsistent because they rely on practitioner experience. We propose a novel fully automated Weakly 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 region from clinical images, removing any irrelevant background information. Then, we implement an end-to-end weakly supervised object detection method. We utilize Vision Transformer (ViT) to process tongue images in 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 visualization experiments 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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2408.16451>)

[PDF](<https://arxiv.org/pdf/2408.16451.pdf>)