Hyperbolic Concept Embedding Model for Interpretable Medical Image Diagnosis

Although deep neural networks (DNNs) have demonstrated strong performance in medical classification, their opaque reasoning undermines the trustworthiness of clinical diagnoses due to limited interpretability. Concept bottleneck models (CBMs) introduce an intermediate concept layer, decomposing black-box prediction into an explicit reasoning path: image → concept → disease. However, concepts and their corresponding diagnostic results in medical image diagnosis are often hierarchical, inclusive, and semantically unevenly distributed. Most concept-based methods represent concepts in Euclidean space and treat them as isolated entities, which hinders the modeling of their structural relationships and thereby reduces diagnostic transparency. To address this, we introduce a hyperbolic concept embedding model (HCEM) that maps medical concepts into a hyperbolic space better suited for hierarchical representation, enabling explicit modeling of complex concept relationships and their semantic associations with diseases. In this model, we regularize the hyperbolic concept embedding space using a positive–negative concept contrastive loss and a concept entailment cone loss. Furthermore, we employ an intervention-aware disease concept hyperbolic embedding regularization to learn their dynamic relationships. It avoids rigid rule priors while improving diagnostic consistency and flexibility under concept interventions. Extensive experiments on four medical datasets validate that our HCEM provides high accuracy in both concept and disease classification, as well as superior interpretability and intervenability. The code will be released soon.