SSC-Net: A multi-task joint learning network for tongue image segmentation and multi-label classification

Traditional Chinese medicine (TCM) tongue diagnosis, through the comprehensive observation of tongue's diverse characteristics, allows an understanding of the state of the body's viscera as well as Qi and blood levels. Automatic tongue image recognition methods could support TCM practitioners by providing auxiliary diagnostic suggestions. However, most learning-based methods often address a narrow scope of the tongue's attributes, failing to fully exploit the information contained within the tongue images.

To classify multifaceted tongue characteristics, and fully utilize the latent correlation information between tongue segmentation and classification tasks, we proposed a multi-task joint learning network for simultaneous tongue body segmentation and multi-label Classification, named SSC-Net.