Hyperbolic vision language representation learning on chest radiology images.

Given the visual-semantic hierarchy between images and texts, hyperbolic embeddings have been employed for visual-semantic representation learning, leveraging the advantages of hierarchy modeling in hyperbolic space. This approach demonstrates notable advantages in zero-shot learning tasks. However, unlike general image-text alignment tasks, textual data in the medical domain often comprises complex sentences describing various conditions or diseases, posing challenges for vision language models to comprehend free-text medical reports.