Weakly Supervised Object Detection for Automatic Tooth-marked Tongue Recognition

• URL: http://arxiv.org/abs/2408.16451v1
• Date: Thu, 29 Aug 2024 11:31:28 GMT
• Title: Weakly Supervised Object Detection for Automatic Tooth-marked Tongue Recognition
• Authors: Yongcun Zhang, Jiajun Xu, Yina He, Shaozi Li, Zhiming Luo, Huangwei Lei,
• Abstract summary: 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 WeaklySupervised method using Vision transformer and Multiple instance learning WSVM for tongue extraction and tooth-marked tongue recognition.
• Score: 19.34036038278796
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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