HiTrace: Hierarchical Class Tracing Approach for Open-Set Recognition on Skin Lesions

In the constantly evolving field of artificial intelligence (AI), identifying unknown or novel classes, known as Open-set Recognition (OSR), is critical for ensuring the reliability of AI models in various applications, especially in the vital field of medical diagnostics. This study introduces a novel approach to advance OSR through the proposed hierarchical class tracing approach, HiTrace, for skin lesion classification. HiTrace incorporates three key components that tackle the complex challenges in OSR: the Hierarchy-Aware Prototype (HAP) learning for an efficient training strategy, the Distribution Enhancement (DE) module for optimized post-processing feature adjustment, and the Potential Class Tracing Algorithm (PCTA) for a hierarchical classification decision-making process. HiTrace leverages a hierarchical taxonomy to simplify the identification of new skin conditions, reducing the need for extensive manual annotation and addressing the limitations of existing methods. This study also introduces two novel evaluation metrics, the Hierarchical Open-set Classification Score (HOC-Score) and Major-Type Accuracy for Open-set samples (MTACC-O), which provide robust criteria for assessing a model's performance in classifying closed-set, in-taxonomy, and out-of-taxonomy results. Notably, the experimental results demonstrate significant advancements in the PAD-UFES-20 and ISIC 2019 datasets, with relative improvements of 15.3% and 21.1% in HOC-Score and 12.3% and 5.8% in MTACC-O, respectively, without compromising on competitive closed-set performance. To the best of our knowledge, this is the first study to present a three-stage analysis (i.e., closed-set, in-taxonomy, and out-of-taxonomy classification results) of OSR applied to a practical medical field. This comprehensive approach represents an influential stride in enhancing patient care through the early detection and treatment of skin diseases, paving the way for future research and development in medical diagnostics and beyond.