Weakly Supervised Object Detection for Automatic Tooth-marked Tongue Recognition
This work addresses the need for objective and accurate diagnosis in TCM, providing clinical value by assisting practitioners, though it is incremental as it applies existing weakly supervised techniques to a specific domain.
The paper tackles the problem of subjective and inconsistent tooth-marked tongue recognition in Traditional Chinese Medicine by proposing a fully automated weakly supervised method, achieving high accuracy in classification and effective region pinpointing.
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.