H2CL: Dual-Geometry Hyperbolic-Euclidean Image-Text Learning for Medical Hierarchical Classification
A UNSW Sydney team built H²CL — a dual-geometry image-text framework that simultaneously operates in Euclidean and hyperbolic spaces, combining group contrastive learning with a hyperbolic entailment loss, to classify medical images across clinical taxonomies. The result: 7% accuracy gains…






