Accurate and Interpretable Wound Healing Progress Detection Based on a Task-Related Knowledge Refinement Learning Method

Juan He, Xiaoyan Wang, Yi Pan, Zhengshan Wang, Bokai Yang, Zhiming Zhang, Tzu-Ming Liu, Yunpeng Cai, Long Chen, Ruitao Xie

Abstract: Automatic detection of wound healing progress is essential for assisting clinicians in evaluating wound conditions, guiding clinical treatment, and preventing infections and complications. However, current methods for detecting wound healing progress are often susceptible to interference from irrelevant information, leading to low detection accuracy, and the existing techniques generally lack interpretability. In this study, we construct a new wound healing dataset, the first to provide continuous temporal observations of skin wound healing status. Based on this dataset, we employ a task-related knowledge learning framework for extracting wound-related features of the skin wound images. Utilizing these features, we design several simple and highly interpretable machine learning models for classification study of the wound healing progress. In comparison to existing deep learning models, which lack interpretability, our simple models achieve superior accuracies in predicting wound healing progress. Furthermore, we analyze the learned wound-related features and find that their spatial distribution aligns with established medical principles, further confirming the features we learn are both interpretable and reliable.