Weakly Supervised Object Detection for Automatic Tooth-marked Tongue Recognition
<|reference_start|>Weakly Supervised Object Detection for Automatic Tooth-marked Tongue Recognition: 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.<|reference_end|>
@article{zhang2024weakly,
    title={Weakly Supervised Object Detection for Automatic Tooth-marked Tongue
  Recognition},
    author={Yongcun Zhang, Jiajun Xu, Yina He, Shaozi Li, Zhiming Luo, Huangwei
  Lei},
    journal={arXiv preprint arXiv:2408.16451},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.16451},
    primaryClass={cs.CV}
}