Hyperbolic Representation Learning for Spatial Biology: Evaluating Cell Type Hierarchies in Breast Cancer Imaging Data

We demonstrate that hyperbolic representation learning effectively captures hierarchical cellular relationships in breast cancer. Using information-theoretic metrics, Lorentzian embeddings are shown to preserve significantly more biologically meaningful structure than Euclidean ones. Code: https://github.com/youssefwally/FlatlandandBeyond.