Semantic-guided Unknown-aware Rare Disease Diagnosis System

The significant challenge posed by rare disease diagnoses has recently motivated researchers to explore computer-aided solutions. While deep learning approaches have shown potential in developing automatic diagnosis systems, their effectiveness diminishes when addressing rare diseases with limited data. Moreover, existing diagnostic models typically fail to detect unknown diseases, making them inappropriate for real-world applications. In this paper, we address the above challenges by proposing a Semantic-guided unknown-aware Rare Disease Diagnosis (SRDD) model. SRDD aims to tackle the performance degradation of classification models in low-data regimes, as well as their inability to distinguish unknown diseases. Specifically, we propose a Semantic-guided Saliency Discovery (SSD) module to explore semantic saliency information within images by aligning image regions with the semantic knowledge embedded within category labels. The image content is subsequently decomposed into semantically related information (SRI) and image instance template (IIT). Then, we design a Reciprocal samples Synthetic Strategy (RSS) to create known and unknown reciprocal points using SRI and IIT. This facilitates a compact feature space for known classes while preserving space for unknown data, promoting accurate known disease diagnosis and unknown disease detection. We validate SRDD on the public skin disease dataset SD-260. SRDD achieves state-of-the-art performance in both known disease classification and unknown disease identification.